Removal of thorium and zirconium from aqueous streams by ... · Sunitha, Dr Rajesh, Saranya, Vidya,...

i

Removal of thorium and zirconium from aqueous

streams by biosorption

A thesis submitted in fulfillment of the requirements for the degree of Doctor

of Philosophy

Sayanasri Varala

B.Tech

M.Tech

Chemical and Environmental Engineering, School of Engineering

College of Science, Engineering and Health

RMIT University

August 2017

ii

Declaration

I certify that except where due acknowledgement has been made, the work is that of the

author alone; the work has not been submitted previously, in whole or in part, to qualify for

any other academic award; the content of the thesis is the result of work which has been

carried out since the official commencement date of the approved research program; any

editorial work, paid or unpaid, carried out by a third party is acknowledged; and, ethics

procedures and guidelines have been followed.

Sayanasri Varala

iii

Acknowledgements

This thesis would never have happened without the help, suggestion, encouragement,

evaluation, and support of many great people. It is a pleasure to thank those who made

this thesis possible and I express my heartfelt appreciation, especially to the following

entities.

I owe my deepest gratitude to my supervisor at RMIT University, Dr Rajarathinam

Parthasarathy, Associate Professor for his detailed and constructive comments, and

invariable aid throughout the thesis work. His valuable advice, logical thinking, friendly

help and extensive discussions have been of very significant for this thesis.

I am delighted to express my profound sense of indebtedness to my Supervisor at CSIR-

IICT, Dr B Satyavathi, Principal Scientist for her kind support, guidance, unfailing

attention and constant encouragement throughout my research work that made this

thesis possible. She has provided her support in many ways, and I am appreciative of

everything she has done for me. I was able to work under her supervision, and I am

obliged for all these years. She has my utmost respect and admiration for introducing to

this research topic, which has been very interesting and given me great insight into the

future work.

It is an honor to be associated with Prof Suresh K Bhargava, Deputy VC and Dr Mark

Pownceby, CSIRO. I have great respect towards Prof Suresh K Bhargava for leading

CSIR-IICT collaboration research program that gave an opportunity to explore multi-

continental research scenario. I gratefully acknowledge the revisions and comments

specified by Dr. Mark in the thesis draft that enriched the standards of the thesis.I

gratefully acknowledge their mentorship during the research work.

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My sincere thanks are expressed to Dr M Lakshmi Kantam, Ex-Director, CSIR-IICT for

providing me with an opportunity to carry out my research work in IICT and providing

financial support through the fellowship program.

I wish to extend my obligations to Mr. D Mallikarjun and Mr. N Balaji for rendering their

technical assistance during the experimental work in IICT. I appreciate the support and

help provided by all technical and administrative staff of IICT and RMIT during the

years. I also acknowledge the moral support rendered by IICT and RMIT friends during

the course of time.

I heartily bestow all my besties’ especially Dr. Sunitha, Rajesh, Saranya, Vidya, Vivek,

Sandeepa and Alka for their ample favours and encouragement even during tough times

in PhD pursuit. Finally, and most importantly, I am delighted for the unending support

of my family for their immense love, patience, and generosity which have been my

strength, inspiration and enthusiasm at every front of my life. They raised me to strive

to be the best at whatever I do but to be humble, recognizing that anything I

accomplished was a gift from God. My academic career would not have come this far

without their full love and encouragement at each step of the way.

Once again I thank everyone.

Sayanasri Varala

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Publications:

1. Sayanasree Varala, Banala Dharanija, B. Satyavathi*, V.V. Basava Rao, R.

Parthasarathy. New biosorbent based on deoiled Karanja seed cake in biosorption

studies of Zr(IV): Optimisation using Box–Behnken method in response surface

methodology with desirability approach. Chemical Engineering Journal 302

(2016) 786-800. Citations-13.

2. Sayanasree Varala, Alka Kumari, B. Dharanija , Suresh K Bhargava, R.

Parthasarathy, B. Satyavathi*. Removal of thorium (IV) from aqueous solutions by

deoiled Karanja seed cake: Optimisation using Taguchi method, equilibrium,

kinetic and thermodynamic studies. Journal of Environmental Chemical

Engineering 4 (2016) 405–417. Citations-6.

3. Sayanasri Varala, R. Parthasarathy, Suresh K Bhargava, B. Satyavathi*.

Desorption studies for the recovery of radionuclides (Th and Zr) and optimisation

using Taguchi mixed design L18 (2132) - A regeneration step for loaded biosorbent,

general mathematical model for multistage operation. Journal of Environmental

Chemical Engineering 5 (2017) 5396-5405.

Conference Presentations:

1. Sayanasri Varala, B. Satya Sirisha, P.V. Aishwarya, R.Parthasarathy, B. Satyavathi*,

“Desorption studies for the recovery of thorium from loaded biosorbent (DKSC):

Parameter optimisation and equilibrium modelling” at International Conference on

Chemical and Biochemical Engineering (ICCBE) held at Pune , India, 2017.

2. Sayanasri Varala, R. Parthasarathy, B. Satyavathi*, “Desorption of zirconium metal

ions from loaded biomass and process optimisation” at Student research Symposium

on Water: Effective Technologies and Tools Research Centre (WETT), RMIT University,

Melbourne, Australia, 2016.

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3. Sayanasree Varala, Alka Kumari, B.Dharanija, R.Parthasarathy, B.Satyavathi,

“Equilibrium, kinetic and biosorption studies of thorium from aqueous solutions using

deoiled Karanja seed cake” held at International Conference on new frontiers in

Chemical, Energy and Environmental Engineering (INCEEE), NIT Warangal, India 2015.

vii

Table of Contents

Declaration i

Acknowledgements ii

Publications v

List of figures xii

List of tables xv

Nomenclature xviii

Abstract Xix

CHAPTER- 1

1. Introduction 1-15

1.1. Research Rationale 2

1.1.1. Source of Th and Zr, their applications, and risks in their

processing

2

1.1.2. Purpose of the present research 5

1.1.3. Research innovation in this work 7

(a) Application of deoiled Karanja biomass as sorbent 7

(b) Optimization of process variables using DOE technique 9

1.2. Research objectives and questions 11

1.3. Structure of the thesis 12

CHAPTER- 2

2. Literature Review 16-27

2.1. Biosorption and desorption techniques 17

2.2 Theories involved 22

2.2.1. Adsorption isotherm models 22

2.2.2. Kinetic models 24

2.2.3. Thermodynamic parameters 26

2.2.4. Desorption kinetic models 27

2.3 Conclusions 27

viii

CHAPTER- 3

3. Materials and Methodology 28-52

Summary 29

3.1. Materials, chemicals, and equipment used in the experimental studies 29

3.2. Pre-treatment and characterisation of DKSC, a new sorbent 30

3.2.1. Physical treatment of Karanja biomass 30

3.2.2. Characterisation of pre-treated biomass 31

(a) Physico-chemical properties 31

(b) Elemental analysis (CHNX) 33

(c) FTIR spectroscopic analysis 34

(d) SEM analysis 35

3.3. Preparation of thorium and zirconium stock solutions 37

3.4. Quantification of thorium and zirconium using UV/Vis

Spectrophotometry

37

3.4.1. Working principle of UV/Vis spectrophotometry 37

3.4.2. Quantification of thorium 38

(a) Xylenol orange solution (10-3 M) 39

(b) Sodium acetate buffer solution 39

3.4.3. Quantification of zirconium 40

(a) Xylenol orange reagent solution (0.05%) 40

3.5. Biosorption and desorption experimental procedures 41

3.5.1. Biosorption experiments for the removal of radionuclides 41

3.5.2. Desorption experiments for the recovery of radionuclides

from loaded biomass

44

3.6. Design of Experiments (DOE) technique for the process optimization 45

3.6.1. Taguchi robust design 46

3.6.2 RSM-Box-Behnken experimental design 48

3.6.3. Percentage contributions 50

3.6.4. Desirability approach for multi-variate optimisation 51

ix

Results & Discussions

CHAPTER- 4

4. Thorium Biosorption and Optimisation Studies via Taguchi and

Desirability Approach

53-71

Summary 54

4.1. Introduction 54

4.2. Experimental investigations 54

4.2.1. Preliminary studies 55

4.2.2. Taguchi L16 (43) OA design 55

4.3. Results and discussions 55

4.3.1. Preliminary investigations 55

4.3.2. Multivariate optimisation of Th(IV) biosorption process using

Taguchi robust L16 design with desirability approach

58

(a) Statistical analysis of Taguchi L16 orthogonal array design 58

(a.1) Effect of initial Th(IV) concentration 60

(a.2) Effect of pH 61

(a.3) Effect of DKSC loading 63

(b) Multivariate optimisation with desirability approach 64

4.3.3. Equilibrium studies and adsorption isotherm modeling 64

4.3.4. Kinetic studies of diffusion and mass transfer modeling 67

4.3.5. Thermodynamic studies for determining feasibility of the

biosorption process

69

4.4. Conclusions 70

CHAPTER- 5

5. Zirconium Biosorption and Optimisation Studies via Box-Behnken

Method in RSM and Desirability Approach

72-89

Summary 73



5.2.1. Preliminary experiments 74

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5.2.2. Box-Behnken design in RSM 74



5.3.2. Multivariate optimisation of Zr(IV) biosorption process using

Box-Behnken method in RSM using desirability approach

77

(a) Statistical analysis of Box-Behnken (33) experimental

design

77

• Interaction effects of process variables 80

(b) Multi-response optimisation via desirability approach 83

5.3.3 Equilibrium studies and adsorption isotherm modeling 84

5.3.4. Kinetic studies with diffusion and mass transfer modeling 87

5.4. Conclusions 88

CHAPTER- 6

6. Desorption Studies for the Recovery of Radionuclides (Th And Zr)

From Loaded-Biosorbent Using Taguchi Mixed Level Design L18 (21 32)

90-102

Summary 91




6.2.2. Taguchi L18 (2132) OA experimental design for metal elution 92



6.3.2. Statistical significance and optimization of desorption using

Taguchi L18 mixed level array design

95

(a) Thorium elution from loaded biomass (Th) 95

(b) Zirconium elution from loaded biomass (Zr) 98

6.3.3. Desorption kinetics evaluation 100

6.4. Conclusions 102

xi

CHAPTER- 7

7. Characterization of Deoiled Karanja Biomass, A Novel Biosorbent for

Radionuclides

103-114


7.2. Materials and methods 105

7.3. Results and Discussions 105

7.3.1. Physico-chemical properties through standard NREL methods 105

7.3.2. Fourier Transform Infrared Spectroscopic (FTIR) analysis 107

(a) Native (pure) DKSC 107

(b) Thorium loaded DKSC (Th-DKSC) 108

(c) Zirconium loaded DKSC (Zr-DKSC) 110

(d) Regenerated DKSC (R-DKSCTh and R-DKSCZr) 111

7.3.3. Scanning Electron Microscopic (SEM) analysis 112

7.4. Conclusions 114

CHAPTER- 8

8. Conclusions and Recommendations 115-119

Appendix 120-123

References 124-131

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List of Figures

Figure 1.1 (a) Karanja fruits (raw) (b) Dried Karanja seeds (c) Dried Karanja nuts

and (d) De-oiled Karanja seed cake.

8

Figure 1.2: General model of a biosorption process. 10

Figure 2.1: General mechanism of biosorption process. 19

Figure 3.1: Procedure for the preparation of biosorbent. 31

Figure 3.2: Major components of a CHNX analyzer. 34

Figure 3.3: Schematic diagram of a typical SEM instrument . 35

Figure 3.4: Schematic diagram showing the principles of UV-Vis

spectrophotometry.

38

Figure 3.5: Calibration curves for Th(IV) and Zr(IV) quantification using UV-Vis

Spectrophotometer; (a) thorium absorbance read at 575nm and (b) zirconium

absorbance read at 535nm.

41

Figure 3.6: Effect of contact time on solution (Th/Zr) pH during biosorption

process.

42

Figure 3.7: Experimental setup used for kinetic and thermodynamic studies in

biosorption and desorption studies.

43

Figure 3.8: Algorithm for Taguchi approach. 47

Figure 3.9: Sequential steps required for RSM. 49

Figure 4.1: Effect of contact time towards bio-removal efficiency (R%) and initial

Th concentration in feed (Ci, mg/L).

57

Figure 4.2: Percentage contributions of process variables towards responses

𝑞𝑒and R%.

59

Figure 4.3: Main effects of major variables (by 𝑆 𝑁 ⁄ ratios) on (a) 𝑞𝑒 and (b) 𝑅% 62

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Figure 4.4: Thorium species distribution diagram. 63

Figure 4.5: qe and Kd as a function of.𝐶𝑒 . 65

Figure 4.6: Validation of equilibrium data through a comparison of different

adsorption isotherm model, (a) Langmuir model and (b) Freundlich model. Error

bars are for ±5 % variation.

66

Figure 4.7: Separation factor (RL) and surface coverage (θ) as function of Ci 67

Figure 4.8: Experimental data (●) and Pseudo-second order model (……..). 68

Figure 4.9: Temperature dependence of thorium biosorption process. 69

Figure 5.1: Preliminary studies: Effect of contact time on zirconium biosorption

onto DKSC.

76

Figure 5.2: Predicted response versus observed response (R%). 78

Figure 5.3: Schematic representation of percentage contribution. 80

Figure 5.4: 3D response surface plots for (a) 𝐴𝐵 with 𝑅%, (b) 𝐴𝐶 with 𝑅% and (c)

𝐵𝐶 with 𝑅%.

81

Figure 5.5: Desirability ramp for numerical optimisation of five goals considered. 84

Figure 5.6: qe and Kd as a function of Ce. 85

Figure 5.7: Adsorption isotherms at optimised conditions (initial pH: 3.6, DKSC

loading: 3 g/L and initial Zr concentration: 18 to 90 mg/L)

86

Figure 6.1: Preliminary studies for desorption of thorium (Th-D%) and

zirconium (Zr-D%) (0.1M concentration, L/S ratio: 1, 200 rpm and 25°C).

94

Figure 6.2: Percentage Contribution of factors for thorium desorption. 96

Figure 6.3: Main effect plots of factors by S/N ratios (larger-is-better) in thorium

desorption.

97

xiv

Figure 6.4: Percentage Contribution of factors for zirconium desorption. 99

Figure 6.5: Main effect plots of factors by S/N ratios (larger-is-better) in

zirconium desorption.

100

Figure 6.5: Desorption kinetics at optimum process conditions. 101

Figure 7.1: 𝛥𝑝𝐻 versus 𝑝𝐻𝑖 for the determination of 𝑝𝐻𝑝𝑧𝑐 of DKSC. 106

Figure 7.2: FTIR spectrum of (a) Pure DKSC, (b) thorium-loaded DKSC (Th-DKSC)

and (c) Regenerated DKSC (R-DKSCTh).

109

Figure 7.3: FTIR Spectra of (a) Pure DKSC, (b) zirconium-loaded DKSC (Zr-DKSC)

and (c) Regenerated DKSC (R-DKSCZr).

110

Figure 7.4: SEM micrographs of DKSC. (a) Raw biomass, (b) DKSC (after

pretreatment), Th-DKSC and (d) Zr-DKSC.

113

xv

List of Tables

Table 1.1: Major sources of thorium 3

Table 1.2: Major sources of zirconium 4

Table 2.1: Merits and demerits of treatment methods employed for treating

radioactive wastes (Wang et al., 2009).

18

Table 2.2: Literature on sorption of Th and Zr using biomass as sorbents 21

Table 2.3: Biosorption (adsorption isotherms and kinetic) and desorption

(kinetic) models

25

Table 2.4: Ranges of thermodynamic parameters and the nature of the

biosorption process.

27

Table 4.1: Factors and levels considered in Taguchi robust design 55

Table 4.2: Taguchi L16 OA design for biosorption of thorium studies with results

obtained

56

Table 4.3: Response Table for signal-to-noise ratios(𝑆 𝑁) ⁄ - Larger is better. 58

Table 4.4: ANOVA table for qe and R% in 𝐿16 OA design. 60

Table 4.5: Parameter values derived from isotherm models. 66

Table 4.6: Kinetic model parameters for thorium biosorption 68

Table 4.7: ∆𝐺0 values for thorium biosorption at different temperatures 70

Table 5.1: Levels of process variables in Box-Behnken experimental design 74

Table 5.2: 33 Box-Behnken design matrix for zirconium biosorption studies with

experimental and predicted results for R%.

75

Table 5.3: ANOVA for response surface quadratic model 79

Table 5.4: Optimisation of individual responses (𝑑𝑖) to obtain overall desirability

response (𝐷)

83

xvi

Table 5.5: Optimised and confirmative values of the process parameters for

maximum responses (R% and 𝑞𝑒)

84

Table 5.6: Isotherm model parameters obtained in the biosorption of Zr(IV) onto

DKSC

86

Table 5.7: Kinetic model parameter values for Zr(IV) sorption onto DKSC 88

Table 6.1: Range of parameters considered in primary research for desorption

studies

92

Table 6.2: Factors and levels considered for Taguchi mixed design𝐿18 (2132). 93

Table 6.3: Taguchi L18 orthogonal array design for desorption process 93

Table 6.4: Response table for S/N ratio (larger-is-better) in thorium desorption

studies

96

Table 6.5: Response table for S/N ratios (Larger-is-better) in zirconium

desorption studies

98

Table 6.6: Kinetic model parameters obtained for thorium and desorption under

optimised experimental conditions.

100

Table 7.1: Physico-chemical properties of DKSC at various stages of biosorption

and desorption processes

105

Table 7.2: Comparision of Shifts in FTIR spectra.

112

xvii

Nomenclature

English

𝐶𝑒 equilibrium metal ion concentration (mg/l)

𝑞𝑒 amount of metal ions adsorbed for gram of adsorbent at

equilibrium(mg/g),

𝑄0 maximum monolayer adsorption capacity(mg/g)

𝐾𝐿 Langmuir isotherm constant (L/mg)

𝑅𝐿 separation factor or equilibrium parameter

𝐶0 initial metal ion concentration (mg/L)

Ɵ surface coverage

𝐾𝑓 Freundlich isotherm constant (mg/g)

N adsorption intensity

𝐴𝑇 Temkin isotherm equilibrium binding constant (L/g)

𝑅 Universal gas constant (8.314J/mol/k)

𝑇 Absolute temperature (298 K)

𝐵 constant related to heat of sorption

𝑞𝑠 theoretical isotherm saturation capacity (mg/g),

𝐾𝑎𝑑 D-R isotherm constant (mol2/kJ2),

𝜀 D-R isotherm constant

𝑞𝑡 amount of metal sorbed at time- 𝑡 (mg/g)

𝑘1 first order rate constant

𝑘2 second order rate constant

ℎ initial sorption rate

𝑘𝑖 intra particle diffusion rate constant

𝐶 Intercept

𝐾𝑑 Distribution coefficient (L/g)

∆𝐻0 enthalpy change

∆𝑆0 entropy change

∆𝐺0 free energy change

𝑘1𝑑 pseudo-first desorption rate constant

𝑘2𝑑 second order desorption rate constant

𝑞𝑡,𝑑 solid-phase concentrations of metal desorbed at any time t

xviii

𝑞𝑒,𝑑 solid-phase concentrations of metal desorbed at equilibrium

w1 weight of Petri dish

w2 initial weight of Petri dish with sorbent

w3 final weight of Petri dish with sorbent

Abbrevations

DKSC De-oiled karanja seed cake

Th-DKSC Thorium loaded biomass

Zr-DKSC Zirconium loaded biomass

Th-DKSCR Regenerated thorium loaded biomass

Zr-DKSCR Regenerated zirconium loaded biomass

RSM Response surface methodology

OA Orthogonal array

AAS Atomic absorption spectroscopy

NREL-LAP National Renewable Energy Laboratory – Laboratory Analytical Procedure

FTIR Fourier Transform Infrared Spectroscopy

SEM Scanning Electron Microscopy

EDS Energy diffraction spectroscopy

pzc Point zero charge

TCD thermal conductivity detector

xix

ABSTRACT

Thorium and zirconium are the most stable radionuclides used in various nuclear

operations, and the separation of these from aqueous industrial streams is essential.

The conventional technologies followed for the treatment of high concentrate nuclear

discharges containing these radionuclides are the precipitation, electro precipitation,

electro coagulation, cementing, membrane separation, solvent extraction, ion-exchange

resins, oxidation–reduction, adsorption, reverse osmosis, and evaporative recovery, etc.

However, afore mentioned treatment methods have certain disadvantages like the high

cost of implementation and operation, especially for the concentrations below 100 ppm.

Hence, the necessity to invent new treatment technologies with acceptable costs is

compulsory for the treatment of low concentrate radioactive wastes. One of the

promising alternatives is the application of biosorption process that utilizes biomass or

bio-based materials as sorbents in the waste water treatment as a pollution control

process for most of the industrial discharge. The advantages of biosorption over the

conventional methods are low operating cost, selectivity for specific metal, short

operational time and no chemical sludge. Biosorption entails the use of living or dead

biomass and their derivatives with the involvement of either ligands or functional

groups (situated on the outer surface of the biomass) in the mechanism of sorption. This

treatment method is based on utilizing the ability of biological materials to accumulate

metal ions from liquid wastes either by metabolically mediated or physicochemical

pathways.

In the present research, an attempt has been made to explore the potentiality regarding

adsorption characteristics of a new agro-industrial by-product namely, de-oiled Karanja

seed cake for the removal and recovery of radionuclide metal ions (Th and Zr) from

xx

aqueous solutions via biosorption method. The relevant process conditions for the

sorption of these metal ions (pH, sorbent mass, ionic concentration, and temperature)

were studied. Furthermore, adsorption isotherm and kinetic sorption modeling,

thermodynamics were investigated to determine the probable physical characteristics

of the biosorption process. Also, the bound metal ions (Th and Zr) were isolated from

the loaded biomass adapting desorption technique using elution agents since

biosorption will be more attractive if loaded biomass can be regenerated for reuse in

multiple sorption cycles. The biosorption and desorption studies were carried out in

batch mode, and the process variables were optimized for the maximum

biosorption/desorption efficiency through DOE concepts like Taguchi OA and RSM. The

property of new biomass was investigated using characterization techniques like SEM,

FTIR, EDX, pHpzc and physicochemical properties.

Deoiled Karanja seed cake has shown good potentiality regarding biosorption capacity

in the removal of thorium and zirconium from aqueous streams, and obtained high Kd

values when compared to commercially available adsorbents, implying an important

feature of DKSC to treat large volumes of low concentration metal wastes. The thorium

equilibrium biosorption data fitted very well to the Langmuir isotherm model, whereas

the zirconium biosorption data fitted the best with Freundlich model representing the

mono-layer sorption and complex heterogeneity of the biomass respectively at

optimum conditions. The sorption kinetic data followed pseudo-second order model

conveying the chemisorption mechanism by the probable involvement of hydroxyl,

carboxyl, amine, and nitro molecular groups. The desorption results revealed that Th

ions could be eluted using 0.1M HCl and 0.1M NaHCO3 can be used for eluting Zr ions

from the loaded biomass respectively. It was also proved that desorption kinetics

follows pseudo-second order model for both thorium and zirconium at optimal

xxi

conditions. Also, the regenerated DKSC was found to possess similar properties as of

native DKSC. Hence, the research work conveys that proposed biosorption/desorption

method using DKSC (new low-cost bi-sorbent) is most cost-effective and efficient

treatment method that is suitable for the effluent treatment of nuclear and

hydrometallurgical industries. Thus, DKSC could be effectively used as a natural and

economic biosorbent for the separation of Th and Zr ions from contaminated sites.

1

Chapter1 Introduction

2

1.1. Research rationale

Henri Becquerel was the first to find about radioactive elements in the 19th century. Ever

since more elements have been investigated for their radioactivity and nuclear

applications. Among them, thorium and zirconium are important radionuclides with

various nuclear applications. The sources, industrial significance, and risks associated

with these elements during their processing are discussed in this chapter. The present

research is focused on determining the most efficient and economical treatment method

that can be used for the isolation of thorium and zirconium from industrial wastewater

streams.

1.1.1. Source of Th and Zr, their applications, and risks in their processing

Thorium is a naturally occurring actinide element with nuclear significance. It is the 41st

abundant metal disseminated over the earth’s crust at an average of 6ppm. It is

represented by the symbol Th and its atomic number is 90. Table 1.1 shows a list of the

major thorium containing ores. Among them, monazite is the one with significant

commercial value. Thorium is mainly refined from monazite-containing heavy

mineral sands and also recovered as a by-product in the extraction of other rare-earth

containing minerals. According to United States Geological Survey (USGS), United States,

Australia, and India have huge reserves of thorium which amount for approximately

25% of the world thorium reserves.

Thirty radioisotopes of thorium ranging from 209 to 238 in mass numbers have

been characterised to date. Among them, 232Th and 230Th are the most stable isotopes

with half-life periods of 14,100 million years and 75,380 years, respectively. 232Th is the

parent primordial radionuclide containing 142 neutrons that accounts for nearly all

natural thorium(Boveiri Monji et al., 2014). It is estimated that thorium is more

3

abundant (about 3 to 5 times) than uranium (Congcong et al., 2014).It is predicted that

thorium will be able to replace uranium in the near future as nuclear fuel in nuclear

reactors.

Table 1.1: Major sources of thorium.

Ore Chemical formula Thwt%

(approx. values) Thorite ThSiO4 71.59

Thorianite ThO2 87.88 Allanite Ca(REE,Ca)Al2(Fe2+, Fe3+)(SiO4)(Si2O7)O(OH) 0.1-2

Monazite (Ce, La, Nd, Th)PO4 4.83 Zircon ZrSiO4 ≤0.4

In thermal breeder reactors, the fertile isotope 232Th is bombarded by slow

neutrons, which leads to neutron capture and the formation of233Th, which further

encounters two consecutive beta decays to become first233Pa and then the fissile 233U as

shown in equation 1.1.

𝑇ℎ90232 + 𝑛 → 𝑇ℎ90

233 + 𝛾21.8 𝑚𝑖𝑛�⎯⎯⎯⎯� 𝑃𝑎91

23327 𝑑𝑎𝑦𝑠�⎯⎯⎯⎯� 𝑈92

2331.5 𝑋 105𝑦𝑒𝑎𝑟𝑠�⎯⎯⎯⎯⎯⎯⎯⎯⎯� (1.1)

233U is fissile and hence can be used as nuclear fuel (as 235U or 239Pu), which can go

through nuclear fission. The neutrons emitted from the fission can strike 232Th nuclei,

restarting the cycle.

Thorium and its compounds and alloys find widespread use in various

applications. It is a main model element for tetravalent actinides (like Np(IV), U(IV), and

Pu(IV)) in natural waters and is useful as a tracer when studying environmentally

important processes (Yusan et al., 2012, Anirudhan et al., 2010).Thorium oxide finds

application as a catalyst, high-temperature ceramic and in high-quality lenses. Thorium

is a gamma-emitting by-product of nuclear reactor operations. It is also a toxic element

that is widely found in various industrial effluents. Some human activities such as the

exploitation of ores associated with thorium, lignite burning in power stations and use

α 𝛽− 𝛽−

4

of fertilizers can also concentrate thorium in the environment, especially surface waters

(Yusan, 2012, Kuber C. Bhainsa, 2009). The effluents containing Th(IV) are known to

cause acute toxicological effects and harmful diseases to humans by localising in the

liver, spleen, and marrow, precipitating as hydroxides and resulting in lung, pancreatic

and liver cancers(Yang et al., 2015, Bhalara et al., 2014)

Zirconium is a solid transition metal with symbol Zr and atomic number 40. It is

a shiny, grayish white material with high metal conductivity, resembling

hafnium mostly and titanium to a lesser extent. The most important sources of

zirconium are tabulated in Table 1.2., among which zircon (ZrSiO4, a silicate material) is

the principal commercial source of zirconium, which is found primarily in Australia,

Brazil, India, Russia, South Africa and the United States, as well as in smaller deposits

around the world. Besides zircon, baddeleyite and kosnarite are also commercially

valuable ores.

Table 1.2: Major sources of zirconium.

Ore Chemical Formula Zr wt%

(approx. values) Zircon ZrSiO4 43.14

Baddeleyite ZrO2 72.03 Zirconia ZrO2 ----

Zirconium is mainly used as a refractory and opacifier and is used in small

amounts as an alloying agent for its strong corrosion resistive traits in aggressive

environments.It is a major engineering material to carry out certain industrial

processes and is mainly used in the manufacturing of photoflash bulbs, moulds for

molten metal’s, surgical appliances, light filaments, watch cases and tanning of

leather(Akhtar et al., 2008). Zirconium-based compounds also have several industrial

applications. For instance, zirconium-based carbides and nitrides are refractory solids;

5

especially carbide is used to make drilling tools and cutting edges. Zirconium dioxide

(ZrO2) is used in laboratory crucibles and metallurgical furnaces. It is also used as a

refractory material in space vehicle parts due to its heat resistance and is sintered into

a ceramic knife. Zircon (ZrSiO4) is a major value added mineral obtained during the

processing of titanium minerals like ilmenite and rutile and is used as gemstones in

jewelry. It is also used as a component in some abrasives like grinding wheels and

sandpaper. The most extensive utilisation of zirconium is in water-cooled nuclear

reactors either in fuel containers or nuclear products to trap fission fragments and

neutrons, thus enhancing the efficiency of the nuclear reactor. 90Zr, 91Zr, 92Zr, 93Zr, 94Zr,

95Zr and 96Zr are the naturally occurring isotopes of zirconium, among which 90Zr is the

most common making up to 51.45% of all zirconium, and 96Zr is the least common

making up only 2.80%.Zr93 and Zr95 are the main untreated wastes of nuclear discharge

released during fission and activation reactions in nuclear reactors and dissolution of

“Zircaloy” fuel cladding. These isotopes (Zr93 and Zr95) have significant value in nuclear

fuel cycle due to their long half-life (1.5×106 years). Zirconium has a complex chemistry

forming [Zr4 (OH) 8(H20)16)] 8+species in the acidic environment, as in waste streams

from nuclear installations, leading to a particulate complex formation above pH 6.0

(Garnham et al., 1993, Akhtar et al., 2008). Due to the large usage of zirconium for its

unique physical and chemical properties in various industries, heavy releases of

zirconium into the surface water occur from several anthropogenic sources including

nuclear power production, ceramic dust, heavy mineral mining, improper waste

dumping, accidentalrelease,e.g., leakage, corrosion and from atmospheric fallout.

1.1.2. Purpose of this research

Several industrial activities dealing with thorium and zirconium produce low,

intermediate and high-level radioactive wastes that require advanced treatment;

6

otherwise, the discharges may potentially pollute the surface water. The exact

treatment and processing of the industrial streams containing these radioactive

nuclides are of a large environmental concerndue to their toxicity and persistence,

which pose severe adverse effects on human and ecological health. Even at trace levels,

these materials have been a public health problem for many years.They can enter the

food chain via bioaccumulation process and disturb the normal functioning of the

ecosystem. Also, the segregation of these radionuclides from aqueous media is an

important subject of hazardous and nuclear waste management due to their toxicity.

The recovery of these ions, therefore, has economic, technical, and commercial

importance owing to their nuclear applications. Strict environmental protection

legislation and public environmental concerns lead to the search for novel techniques

for the recovery of radionuclides from industrial waste water.

Chemical/electro-precipitation, electro floatation, electro deposition,

evaporation, ion exchange, reverse osmosis, solvent extraction, membrane separation,

and adsorption are some of the conventionally followed separation techniques for the

removal and retrieval of radioactive ions (Akkaya et al., 2013, Kütahyalı et al., 2010).

Nevertheless, most of these methods are only suitable for large scale treatments and

incur a high cost when practiced. Also, they have serious drawbacks such as poor

efficiency when they are present at low concentrations (<100 mg/L) necessitating the

use of expensive chemicals and accompanying disposal problems. Treatment of dilute

wastes (<100 mg/L) is necessary since concentrations at this level are potentially toxic

and hazardous to human beings (Ioanna et al., 2013). New technologies with acceptable

costs are required for the reduction of these low concentration radioactive ions in

industrial effluents.

7

Compared with conventional methods, biosorption has several advantages and is

considered to be quite attractive based on its efficiency. It is one of the easiest, safest

and most cost-effective methods because it involves simple operation and easy handling

(Siti et al., 2013). The first major challenge for the biosorption operation is to select the

most promising biomass type from an extremely large pool of readily available and

inexpensive biomaterials. The published works on testing and evaluating the

performance of biosorbents in pollution remediation offered a good basis in the search

for new and potentially feasible metal biosorbents. Another challenge is that the

application of biosorption is facing great difficulties for many reasons like lack of

knowledge on the biomass characteristics as sorbent, parameters influencing the

biosorption process and sorbent regeneration and reuse. Great efforts have to be made

to improve biosorption processes, including immobilisation of biomaterials,

improvement of regeneration and re-use, optimissation of biosorption process,etc.

1.1.3. Research Innovation in this work

(a) Application of deoiled Karanja biomass as sorbent

A notable and growing trend is to evaluate the feasibility and suitability of natural, viable,

renewable and low-cost materials, which can be used as sorbents to combat the menace

of metal pollution. Researchers have examined various biomasses for their potential to be

used as sorbents in the removal of metals/organics and classified them into the following

categories: bacteria, fungi, yeast, algae, industrial wastes, agricultural wastes and other

polysaccharide materials. Much literature has been generated in recent years for the

removal of metals from industrial wastewaters using biosorption (Carolin et al.,

2017).Recently, agroindustrial wastes have received significant attention due to their

abundance in nature, biodegradability, eco-friendly and low cost and they can be used as

important sorbents in the removal of metal ions. The annual harvest and processing of

8

various crops in India yield considerable quantities of agricultural by-products. There is

no consistent statistical information about the crops and the associated by-products

generated by these plants in India. Some of the agroindustrial wastes include peat, wood,

pine bark, banana pith, soya bean, cottonseed hulls, peanut shells, rice husk, sawdust,

wool, orange peel, and compost and leaves.

Figure 1.1: (a) Karanja fruits (raw) (b) Dried Karanja seeds (c) Dried Karanja nuts and

(d) De-oiled Karanja seed cake

Deoiled Karanja seed cake is one such biomass with little or no economic value

that is produced as a residue after extracting oil from Karanja seeds. The bio-oil produced

from Karanja is a superior substitute feed for biodiesel production in developing

countries such as India. Karanja seeds (Fig. 1.1) are acquired from the Karanja tree, which

is known as Milletiapinnata, It belongs to the leguminaceae species pea family, fabaceae

and is a resident of tropical and temperate Asia, Australia, and some Pacific islands. It is

regularly known by the synonym Pongamiapinnata and commonly called Karanja in

(a) (b) (c)

(d)

9

India. It is one of the few nitrogen fixing trees and produces bean-like brownish-red seeds

(when dried)about 1.5–2.5 centimeters long. The seeds are brittle and contain28-34% of

oil with a high percentage of polyunsaturated fatty acids (Muktham, 2016).

The Karanja tree and its seeds, the bio-oil produced from Karanja and the de-oiled

Karanja seed cake all have multiple benefits. The plant has been historically used as a

medicinal plant in Ayurveda and Siddha systems of Indian medicine. The Karanja tree is

famous for its shade, ornamental value, seed oil, and plant fodder. It is a part of social

forestry in India. Most parts of the Karanja plant such as the leaves, roots, and flowers,

due to their medicinal properties, are used as a crude drug for the treatment of tumors,

piles, skin diseases, itches, abscess, painful rheumatic joints, wounds, ulcers, diarrhea, etc.

In India, the oil has many applications such as a herbal medicine for the treatment of

human and animal skin diseases, in soap making, in tanning industries and mainly as a

substitute feed for biodiesel. The Karanja seed cake, due to its ample protein and nitrogen

contents and insecticidal and nematicidal activities, finds application as green manure in

agriculture and environmental management (Dwivedi et al., 2014).

In the present research, deoiled Karanja biomass has been used as the sorbent for the

separation of radionuclides namely, thorium and zirconium from aqueous solutions via

biosorption and desorption methods.

(b) Optimisation of process variables using DOE technique

The use of an abundant agro-industrial waste based sorbent for the removal of metals

cannot ensure an efficient process. There is a need to optimise and organise process

variables to obtain the desired and effective outputs. In developing a process, it is

important to understand the influence of process parameters and their interactions on

10

process performance to determine an optimum set of parameters that will lead to the

desired outputs. The classical style of trial and error (change one factor at a time)

approach to determine the optimum set of process parameters has many drawbacks. It

is time-consuming. There will be a lack of information in this method about the

interactions among the variables especially when the number of possible process

parameters is high.

On the other hand, Design of Experiments (DOE) technique can help to

determine the minimum number of experiments consisting of a possible parameter

combination and suggest parameter domains where the process offers the most benefit.

DOE is a series of runs/tests that involve purposeful changes to input variables and aids

in observing the change in responses at the same time. The main criteria that need to be

considered while picking an appropriate DOE that produces the best response include:

(i) identifying the number of control factors with their respective levels, (ii)

determining the least possible number of runs that can be performed, and, (iii) verifying

the impact of cost, time, and availability of chemicals (Douglas 5th edition).

Figure 1.2: General model of a process.

Controllable factors x1 xp x2

Process Outputs Inputs

Uncontrollable factors

zq z2 z1

y

11

A designed experiment is a series of runs/tests in which the experimenter

purposefully makes changes to input variables and observes the responses. In general,

experiments are used to study the performance of a process that can be represented by

a model as shown in Figure 1.2.

The process consists of desired serial procedures that are required to transform

an input into an output that has one or more observable responses. Some of the process

variables x1, x2… xp are controllable, whereas others (z1, z2… zq) are uncontrollable. The

general objectives of the experiment include: 1) determining the most influential

variables on the response y, 2) determining where to set the influential x’s so that y is

almost always near the desired nominal value and the variability of y is small, and 3)

minimizing the effects of uncontrollable variables z1, z2, …, zq. Hence, the experimenter’s

objective is to plan, conduct and determine the influence of the above factors on the

output response of the system. DOE offers the following experimental designs: factorial

design, response surface design (RSM), mixture design and Taguchi design.

1.2. Research objectives and questions

The objectives of the present study are

• To examine the potential for using deoiled Karanja biomass as a sorbent for the

removal of radionuclides from aqueous solutions

• To obtain equilibrium, kinetic and thermodynamic data to understand the

biosorptivebehaviour of Karanja biomass

• To optimise the biosorption process using the principles of design of experiments

(DOE)

12

By conducting this research, the following research questions can be answered:

• What is the efficiency of deoiled Karanja biomass as sorbent in the removal of

thorium and zirconium from aqueous solutions?

• What quantity of biomass is required for the treatment of aqueous streams

containing radionuclides?

• Is desorption with acidic solutions are proficient for the isolation of thorium from

loaded biomass?

• Which eluting agent is the best for the recovery of zirconium by desorption?

• Which of the Taguchi and RSM design approaches gave best the results in process

optimisation?

1.3. Structure of the thesis

Chapter 2: Literature review

This section summarises the biosorption method and the theories involved in the concept.

Also, it describes the ability of various biomasses in the removal of thorium and

zirconium from aqueous and industrial streams. The application of DOE concept in the

optimisation of biosorption process is also discussed.

Chapter 3: Materials and methodology

This chapter summarises the experimental procedures followed in the pre-treatment of

biomass, biosorption and desorption studies. The techniques adapted for the

characterisation of deoiled Karanja biomass and the principles of Taguchi and RSM in

DOE are discussed.

13

Chapter 4: Thorium biosorption and optimisation studies using Taguchi and

desirability approach

This chapter discusses the adsorption characteristics of deoiled Karanja biomass which

has been employed as a sorbent in the biosorption studies for the removal of thorium

metal ions from aqueous solutions. It discusses the effects of process variables and

explains the optimisation of parameters used to achieve the maximum biosorption

capacity and bio-efficiency using Taguchi robust design, L16 (43) orthogonal array and

desirability approach in multi-variate optimisation for the simultaneous maximisation

of responses. Chapter 5: Zirconium biosorption and optimisation studies using Box-Behnken

method in RSM and desirability approach

The chapter discusses the separation of zirconium from aqueous solutions using deoiled

Karanja biomass as sorbent. It also discusses the effects of process variables. It explains

the optimisation of parameters for achieving the maximum biosorption capacity and bio-

efficiency using the Box-Behnken method with 33design in response surface methodology

(RSM). It discusses how the same responses are maximised employing a desirability

approach in multivariate optimisation.

Chapter 6: Desorption and optimisation studies for the recovery of radionuclides (Th

and Zr) from loaded-sorbent using Taguchi mixed level design

This section describes the mechanism underlying the desorption of Th and Zr from loaded

biomass. It discusses the influence of the process parameters in achieving the maximum

desorption. It also presents the Taguchi mixed level design 𝐿18 (2132)that was used in the

optimisation of the process variables for maximum recovery efficiency.

14

Chapter 7: Characterisation of deoiled Karanja biomass, a novel sorbent for

radionuclides

The chapter describes the characterisation of Karanja biomass which was employed in

the current research as biosorbent. It discusses the results of characterisation

techniques such as SEM, FTIR and standard NREL methods which help to investigate the

possible mechanisms of the biosorption and desorption of Th(IV) and Zr(IV).

Chapter 8: Conclusions and recommendations

This chapter summarises the major findings of the present research and recommends

possible future studies in this area.

15

Removal of thorium and zirconium from aqueous streams by biosorption

Utilising agro-industrial waste biomass as sorbent in the biosorption studies

Application of DOE concept

for process optimization

Chapter 4 • Thorium biosorption

studies • Process optimisation via

Taguchi L16 OA • Langmuir isotherm model

and pseudo-second order kinetic model validation

Chapter 5 • Zirconium biosorption studies • Process optimization via Box-

Behnken method in RSM • Freundlich isotherm and

pseudo-second order model validation

Chapter 6 • Isolation of Th/Zr from loaded

biomass through desorption • Process optimization via

Taguchi L18 OA • Pseudo-second order kinetic

model validation

Chapter 7 • Characterisation of biomass • NREL-LAP, physico chemical

properties • SEM, FTIR and EDX analysis

Chapter 8 • Conclusions • Future recommendations

16

Chapter2 Literature Review

17

Thorium and zirconium are toxic and shows adverse effects. Thus, industries that use

these materials are advised to treat their radioactive wastes systematically so that the

extent of active sites can be minimised. Treatment methods for the removal of most

radioactive wastes include physical, chemical and biological technologies. A summary of

conventional methods that are used in the treatment of industrial streams containing

radioactive wastes is given in Table 2.1 with their merits and demerits.

2.1. Biosorption and desorption techniques

Biosorption has been universally accepted as one of the most efficient pollutant removal

processes with several advantages especially for industrial discharges with low

pollutant concentration (Table 2.1). Biosorption is based on utilising the ability of

biological materials including microorganisms (living or dead organisms), agricultural

and vegetable wastes as sorbents to remove the metal ions/ pollutants from liquid

wastes. It is the adsorption characteristics of biomasses that enable the binding and

concentrating of the metal ions from dilute aqueous solutions (Gok and Aytas, 2013).

A general mechanism involved in the removal of pollutants in a typical biosorption

method is shown in Figure 1.1. The biosorption process includes a solid phase (sorbent

or biosorbent; biological material) and a liquid phase (solvent, generally water)

containing the dissolved species to be sorbed (sorbate, metal ions). The solid-liquid

mixture is agitated at controlled conditions until equilibrium is established between the

solid-bound sorbate species and its portion remaining in the solution.

18

Table 2.1: Merits and demerits of treatment methods employed for treating radioactive

wastes (Wang et al., 2009).

Technology Merits Demerits Ion exchange • Commercially available

• Effective on co-occurring contaminants

• Well-understood, well-accepted by metal industry

• Resin regeneration and replacement is costly

• Not efficient on all metals • Produces metal-laden waste

brine • Overall, high initial, capital,

and operational/maintenance costs

• Cannot be used for large scale • Ineffective for low

concentrated wastes Reverse osmosis • Effective removal method

• Accepted benchmark technology • Capital intensive • Low throughput • Produces metal-laden waste • Membranes are expensive • Quickly foul up • Elevated pressure • Sensitivity to suspended

solids and organics Chemical Precipitation

• Effective • Low capital cost • Simple operation • Non-metal selective

• Inadequate • Requires tight operational

controls • Post-treatment needs are

required • Secondary sludge generation • Ineffective for 1-100 mg/L

Biosorption • Inexpensive • Metal selectivity • Smooth operation • High efficiency • High versatility • Tolerance to contaminants • results in small volumes of high-

concentration wash solutions suitable for subsequent metal recovery

• Free of secondary pollution

• Not very well understood • Difficulty in developing

generic technologies

Membrane filtration

• Less production of solid waste • Low chemical consumption

• High initial capital and maintenance costs

• Low flow rates • Cannot be used for large scale • Ineffective for low

concentrated wastes Electrochemical treatment

• Metal selective • Potential treat effluent > 2000

mg/L

• High initial capital cost, • Ineffective for 1-100 mg/L

19

Figure 2.1: General mechanism of biosorption process

Due to higher affinity between the sorbent and sorbate species, the latter is

attracted and bound to the sorbent surface by one or more possible combinations of

mechanisms such as ion exchange, complexation, coordination, adsorption, electrostatic

interaction, chelation and micro precipitation either by physical bonding via London-

Vander Waals forces or electrostatic attraction, or by chemical bonding such as ionic or

covalent bonding.The underlying mechanism in biosorption is yet to be understood

especially if the biomass is a derived material. The degree of sorbent affinity towards

the sorbate determines its distribution between the solid and liquid phases. Studies

have shown that the biosorption mechanisms depend on the type of functional groups

on the surface of the biomass, the nature of the metal, and the characteristics of the

matrix around the sorbent species (Boveiri Monji et al., 2008).

Biosorption has gained most recognition for sequestration of radionuclide metal

ions (Th and Zr) from aqueous effluent streams due to its excellent separation

characteristics. Till date, numerous authors have investigated the ability of various

kinds of biomass in the separation of thorium and zirconium from aqueous streams

(Table 2.2). Most of the research used synthetic thorium and zirconium metal ions in

aqueous media (prepared from their respective nitrate/chloride salts) as feed to

20

determine the metal uptake capacity of various biosorbents. Only a few have used real

waste samples such as sea water containing 100 mg/L of Th in 1L of sea water

(Anirudhan et al., 2010) and a Th-rich monazite mineral (river sand type) (Boveiri

Monji et al., 2014). Their findings have demonstrated that biosorbents are not effective

in the removal process probably due to the interference from other cations, for the

sorption sites on biosorbents. The results of thorium biosorption studies showed that

the process is mainly influenced by the initial pH of the feed solution and the

biosorption efficiency is the highest in the pH range of 4 to 6 (Sayanasree et al., 2016).

Zolfonoun et al. (2010) demonstrated the adsorptive characteristics of rice bran for the

removal of zirconium ions from leach liquor containing zircon concentrate. They

reported that the efficiencies of adsorption of other allied metal ions such as Ti (IV), La

(III), Ce (III), Al (III) and Fe (III) were considerably lower than that of Zr (IV) adsorption.

They therefore concluded that rice bran is an excellent sorbent for the selective uptake

of zirconium from acidic aqueous solutions.

The method of using biosorption from aqueous effluent streams can be applied

to a low-cost water pollution control process for the efficient recovery of bound metal

ions from industrial discharge and the subsequent regeneration of biosorbent(Kuyucak

and Volesky, 1989, ALDOR et al., 1995). Desorption of the metal ions from the loaded

biosorbent is accomplished using an elution process that involves the use of an

appropriate eluting/desorbing medium to elute metals from the loaded biosorbent. This

results in a small, concentrated volume of metal in the eluting solution after the

desorption (Njikam and Schiewer, 2012, Vı´tor J.P. Vilar, 2007). Desorption process is

metal-selective, economically viable, and ensures the recovery of bulk metals which can

be reused in process applications. It also simplifies the disposal and discarding

21

problems due to the effective reuse of biomass in multiple cycles (Diniz and Volesky,

2006).

Table 2.2: Literature on sorption of Th and Zr using biomass as sorbent.

Radionuclide Type of sorbent qe R% Reference Th(IV) Microporous

Composite P(HEMA-EP) 0.44 -- (Akkaya and Akkaya, 2013)

Poly(methacrylic acid)-grafted chitosan/bentonite composite matrix

110.5 -- (Akkaya and Akkaya, 2013, Anirudhan et al., 2010)

S. Sporoverrucosus. Dwc-3

-- >90% (Ding et al., 2014)

Fusarium sp. #ZZF51 (living biomass) -- 79.5% (Yang et al., 2015) Rhizopusarrhizus 180 -- MariosTsezos and

BohumilVolesky, 1981 Alginate biopolymers -- 94% (Gok and Aytas, 2013) Rice bran

49.3

>90

(Boveiri Monji et al., 2014)

Wheat bran 39.7 >70

Activated carbon (olive stones) 87 -- (Kütahyalı and Eral, 2010) Aspergillusfumigatus 71.94 -- Kuber C. Bhainsa et al., 2009 Bacillus sp. Dwc-2 10.75 -- Lan etal., 2015 Bone Meal – 15435 11.5 -- Eduardo et al., 2015 Actinomycetes -- -- Akira et al., 2002 Hyacinth roots -- -- Ashraf Aly, 2014

Zr(IV) Microalgae --- --- (Garnham et al., 1993) Cyanobacteria -- -- Candida tropicals 179 -- (Akhtar et al., 2008) Aspergillusniger (living) 78.8 -- Kalantari et al.,2014 Aspergillusniger (dried) 142 -- Coriolus versicolor 24.35 -- (Bhatti and Amin, 2013) Rice bran 50 -- (Zolfonoun et al., 2010) Rice Bran 48.30 99.3 (Boveiri Monji et al., 2008)

Wheat Bran 34.72 98.5 Platanus orientalistree leaves 29.49 92 Sugar cane bagasse 107 -- Abida Kausar et al., 2016

111 -- 71.5 --

The mechanisms of desorption and biosorption are similar with both involving

ion exchange, i.e., a substitution reaction between anions and cations over the active

sorption sites. If the sorption comprises physical bonding, the loosely bound sorbate can

22

easily be desorbed using distilled water. However, if chemical bonding is present in the

sorption mechanism, then the sorbed metal ions can be recovered using proton exchangers

or chelating agents. This is especially the case when diverse functional groups such as

hydroxyl, carbonyl, carboxyl, amine, amide, and phosphonate groups participate in the

adsorption process. The adsorption mechanism with these groups is not restricted to

physical bonding (Jnr, 2006).

Although the biomass can be reused after desorption, proper care needs to be

taken as the biomass may deteriorate when it is exposed to strong reagents during the

recovery process. Hence, the nature and strength of the desorbing medium need to be

considered to ensure the reusability of the biomass and minimization of any physico

chemical damage. The research related to isolation of radionuclides (Th and Zr) from

loaded biomass is scarce. Very few researchers have studied the desorption process

using various eluting agents (Bhalara et al., 2014, Gok and Aytas, 2013, Garnham et al.,

1993, Akhtar et al., 2008, Kuber C. Bhainsa, 2009). The above studies on desorption

have reported that HNO3, HCl, NaHCO3, and NaCl solutions exhibit high desorption

efficiency in the elution process for the recovery of Th and Zr from loaded biomass.

2.2 Theories involved

2.2.1 Adsorption Isotherm Models: Adsorption equilibria are the most desired

evidence for proper design and analysis of adsorption data, and they can be achieved

through adsorption isotherms. During biosorption, a rapid equilibrium is established

between the amount of metal adsorbed on the sorbent (qe) and the metal remaining in

the solution (Ce). There are many theories/models available relating these parameters

to illustrate the biosorption phenomena. Among them, the Langmuir, Freundlich,

Temkin, and D-R adsorption isotherms are the classical models that are most frequently

23

used to describe the relationship between equilibrium concentrations. These are

provided in Table 2.3 (equations 2.1 to 2.4) and described below:

(a) Langmuir adsorption isotherm: This model describes the coverage of molecules on

the outer surface of the sorbent quantitatively. The model is based on three

assumptions; adsorption is limited to mono layer coverage, all surface sites are alike

and only can accommodate one adsorbed atom, and the ability of a molecule to be

adsorbed on a given site is independent of its neighbouring sites. Based upon these

assumptions, Langmuir has derived the model (Langmuir, 1918).

(b) Freundlich adsorption isotherm: This model is commonly used to describe the

adsorption characteristics of complex surfaces. It assumes the heterogeneous

distribution of energetic, active binding sites over the sorbent as well as the interactions

between the adsorbed molecules (sorbate) and sorbent (Freundlich, 1906). In the

Freundlich model, the smaller the value of 1 𝑛⁄ (larger value of n), the greater the

expected heterogeneity. A 1 𝑛 ⁄ value below 1.0 indicates normal adsorption, whereas a

value above 1.0 indicates co-operative adsorption. If 𝑛 lies between 1 and 10, the

sorption process is considered favourable.

(c) Temkin isotherm model: This model assumes that the heat of sorption (as a

function of temperature) for all molecules within the coverage of the adsorbent surface

decreases linearly rather than logarithmically. The adsorption is characterised by a

uniform distribution of bonding energies (up to a maximum bonding energy) and is

determined by plotting the quantity sorbed, qe, against lnCe. The constants are

determined from the slope and intercept (Temkin and Pyzhev, 1940).

(d) D-R isotherm model: The D-R isotherm is used to express the adsorption

mechanism onto a heterogeneous surface using a Gaussian energy distribution. If the

24

mean characteristic energy (obtained from the D-R model) falls within the range of 1 to

16kJ/mol, it signifies the involvement of physical electrostatic forces in the sorption

process. One of the unique features of the D-R isotherm model is that it is temperature

dependent. The approach is usually applied to distinguish the physical and chemical

adsorption of metal ions with its mean free energy, E per molecule of adsorbate (for

removing a molecule from its location in the sorption space to the infinity) (Dubinin and

Radushkevich, 1947).

2.2.2 Kinetic Models: Kinetics plays a significant role in studying the mechanism

involved in the biosorption process and is concerned with the rate-limiting steps that

include processes such as mass transport and chemical reaction. Various models are

available to analyse the kinetics of sorption process. They are: pseudo-first order,

pseudo-second order, intra particle diffusion and Elovich models (equations 2.5 to 2.8)

Kinetic models are typically used to select the optimum parameter conditions for full-

scale batch metal removal processes through their intrinsic phenomenological rate

coefficients. Each of the models is discussed below:

(a) Pseudo-first order model: This kinetic model, proposed by Lagergren (Lagergren,

1898), is widely used for relating the sorption of liquid on solid and is based on solid

capacity (Azizian, 2004).

(b) Pseudo-second order model: This model is also based on the sorption capacity of

the solid phase, and it assumes that the adsorption mechanism is the rate controlling

step. The correlation coefficient of the model indicates the level of correlation between

the parameters (Plazinski et al., 2013).

25

Table 2.3: Biosorption and desorption models.

Equation No.

Nonlinear form Linear form

Adsorption isotherm models

2.1.

Biosorption

Langmuir 𝑞𝑒 =𝑄0𝐾𝐿𝐶𝑒

1 + 𝐾𝐿𝐶𝑒

1𝑞𝑒

= 1𝑄0

+1

𝐾𝐿𝑄0𝐶𝑒

2.2. Freundlich 𝑞𝑒 = 𝐾𝑓𝐶𝑒1 𝑛⁄ log 𝑞𝑒 = 𝑙𝑜𝑔𝐾𝑓 +

1𝑛𝑙𝑜𝑔𝐶𝑒

2.3. Temkin 𝑞𝑒 =𝑅𝑇𝑏

ln(𝐴𝑇𝐶𝑒) 𝑞𝑒 =𝑅𝑇𝑏𝑇

𝑙𝑛 𝐴𝑇 + 𝑅𝑇𝑏𝑇

𝑙𝑛 𝐶𝑒

2.4. D-R 𝑞𝑒 = 𝑞𝑠 𝑒𝑥𝑝(−𝐾𝑎𝑑𝜀2) Ln 𝑞𝑒 = ln 𝑞𝑠 − 𝐾𝑎𝑑𝜀2

Where 𝜀 = 𝑅𝑇 ln �1 + 1𝐶𝑒�

Kinetic models

2.5. Pseudo-first

order 𝑞𝑡 = 𝑞𝑒(1 − 𝑒−𝑘1𝑡)

log(𝑞𝑒 − 𝑞𝑡) = 𝑙𝑜𝑔 𝑞𝑒

− 𝑘1

2.303𝑡

2.6. Pseudo-second

order 𝑞𝑡 =𝑘2𝑞𝑒2

1 + 𝑘2𝑞𝑒𝑡

𝑡𝑞𝑡

= 1

𝑘2𝑞𝑒2+

1𝑞𝑒𝑡

2.7. Intraparticle

diffusion --- 𝑞𝑒 = 𝑘𝑖𝑡0.5 + 𝐶

2.8. Elovich --- 𝑞𝑡 = 𝑙𝑛𝛼𝐴𝛼

+ 1𝛼

ln t

2.9.

Desorption

Pseudo-first order

𝑑𝑞𝑡,𝑑

𝑑𝑡= −𝑘1𝑑(𝑞𝑡,𝑑 − 𝑞𝑒,𝑑)

log�𝑞𝑡,𝑑 − 𝑞𝑒,𝑑� = 𝑙𝑜𝑔�𝑞0,𝑑

− 𝑞𝑒𝑑,𝑚�− 𝑘1𝑑𝑡

2.10. Pseudo-second

order

𝑑𝑞𝑡,𝑑

𝑑𝑡= −𝑘2𝑑(𝑞𝑡,𝑑 − 𝑞𝑒,𝑑)2

𝑡𝑞𝑡,𝑑

=−1

𝑘2𝑑𝑞𝑒𝑑,𝑚2 +

𝑡𝑞𝑒𝑑,𝑚

(c) Intra-particle diffusion model: This is a complex mathematical relationship

proposed by Weber and Morris (Weber and Morris, 1963) as a function of the geometry

of the biosorbent particle. If intra particle diffusion is the rate limiting step, the metal

uptake varies proportionally with the half power of the time,𝑡0.5 rather than𝑡. The

intercept of the diffusion plot (qe versus t0.5) gives an idea about the thickness of the

boundary layer. The larger the value of the intercept, greater the boundary effect. The

26

diffusion plot shows multi linearity in the biosorption process and contains three stages

as given below,

(i) Diffusion of sorbate through the solution to the external surface of the sorbent.

(ii) Gradual sorption and intra particle diffusion which is the rate limiting step.

(iii) Intra particle diffusion slows down due to an extremely low concentration of

metal ion left in the solution and a reduction in the number of active interior

sites present on the sorbent. This phenomenon eventually leads to the

attainment of equilibrium (Ofomaja, 2010).

2.2.3 Thermodynamic Parameters

Biosorption can be considered as a complex and reversible process at equilibrium. With

regards to adsorption processes, its thermodynamics represents the final state of a

system. The thermodynamic parameters such as Gibbs free energy, enthalpy and

entropy play a vital role in designing separators for industrial biosorption processes.

These parameters are calculated at different temperatures. The enthalpy change (∆𝐻0),

entropy change (∆𝑆0), and free energy change (∆𝐺0) are estimated using the following

equations.

ln𝐾𝑑 =∆𝑆0

𝑅−∆𝐻0

𝑅𝑇 (2.11)

∆𝐺0 = ∆𝐻0 − 𝑇∆𝑆0 (2.12)

The values of ∆𝐻0and ∆𝑆0aredetermined from the slope and intercept

respectively, of the plot of ln𝐾𝑑 vs. 1 𝑇⁄ . Gibbs free energy is then calculated using Eq.

(2.12). The calculated values of the thermodynamic parameters (∆𝐺0,∆𝐻0and ∆𝑆0) are

examined to verify spontaneity, feasibility and the nature of the process. The nature of

27

the biosorption process can be specified by the thermodynamic parameters as tabulated

in Table 2.4.

Table 2.4: Ranges of thermodynamic parameters and the nature of the biosorption

process.

Range Nature of Process ∆𝑮𝟎<0 feasible and spontaneous ∆𝑮𝟎>0 not feasible and non-spontaneous ∆𝑯𝟎>0 endothermic ∆𝑯𝟎>0 exothermic ∆𝑺𝟎>0 increase in randomness

2.2.4. Desorption kinetic models: The kinetic equations proposed by Tseng (2009) to

describe the desorption mechanism are shown in Table 2.3 (equations 2.9 and 2.10).

2.3. Conclusions:

Although various biomasses have been described in the literature for the separation of

radionuclides from aqueous streams via biosorption, studies using agro-industrial

waste based biomasses as sorbents are limited. Furthermore, only a few researchers

have attempted the isolation of the radionuclides from loaded biomass and also for the

optimisation of process variables.

Thus, in the present research work, application of an agro-industrial byproduct

as sorbent for the sequestration of radionuclides (Th and Zr) from aqueous solutions via

biosorption and desorption studies and simultaneous optimisation of process variables

through DOE techniques have been explored.

28

Chapter3 Materials and

methodology

29

Summary

This chapter discusses the materials and equipment used in the present research. It also

describes the experimental procedures followed for the biosorption and desorption studies.

Furthermore, the DOE techniques adapted for the optimisation of process variables are

discussed. The equations involved and the results expressed are also discussed in detail.

3.1. Materials, chemicals, and equipment used in the experimental

studies

Deoiled Karanja biomass was acquired from Maruti Agro Chem., Hyderabad, India. The

following chemicals were purchased from SD Fine-Chem Limited, India; zirconyl chloride

octahydrate (ZrOCl2.8H20, LR Grade), xylenol orange tetra sodium salt dry powder

(C31H28N2O13SNa4), ethanol (99% assay), glacial acetic acid (EP grade), sodium acetate

anhydrous extra pure (98% assay), sodium chloride extra pure (99.5% assay), sodium

hydroxide pellets purified (97.0% assay), sodium carbonate (93% assay), sodium

bicarbonate (96% assay) and Whatman filter paper no. 40 (ashless). Thorium AAS

standard (1000 µg/µl in 5% HNO3) and zirconium AAS standard (1000 µg/µL Zr in

HNO3: HF) were procured from Inorganic Venture, USA and supplied by Crystal

Scientific, India. Thorium nitrate pentahydrate (Th(NO3)4.5H2O, reagent grade) and nitric

acid (69-71% assay) were obtained from Finar chemicals (India) Private Limited.

Hydrochloric acid (35-37% assay) and sulphuric acid (98% assay) were supplied by

Molychem, India. All chemicals were used in the experiments as received without any

modifications.

A Radleys- RR98072 magnetic stirrer was used for washing the Karanja biomass,

and a Jeio Tech model OV-12 vacuum oven was used for drying the Karanja biomass in

the pre-treatment method. A high precision Citizen balance with an accuracy of ±0.0001g

30

was used for weighing. A Sartorious PB-11 pH meter was utilized to measure the pH of

the samples and a water bath supplied by Sun Labtek Equipment India Pvt Ltd. was used

for boiling the samples. The equilibrium studies were carried out using a shaking

incubator (Model No.-LSI4018R) provided by Daihan Labtech India Pvt Ltd., capable of

maintaining the temperature within ±0.1⁰C. A Lab Companion RW-2025GA refrigerated

and heating bath circulator was usedfor maintaining a constant temperature of the

experimental apparatus, and a Heidolph (RZR 2021 model) overhead motor and PTFE

anchor type impeller (75mm dia) was used for the kinetic and thermodynamic studies. A

vacuum pump procured from Heidolph Instruments GmbH & Co., Germany, was used for

the vacuum filtration of the loaded biosorbent in the desorption studies.

3.2. Pre-treatment and characterisation of DKSC, a new biosorbent

3.2.1. Physical treatment of Karanja biomass

The purchased biomass was washed with distilled water multiple times at ambient

conditions to remove dirt, metallic and soluble impurities. The washed biomass was

then sun dried for 2-3 days to remove excess moisture. The sun dried biomass was then

grounded using a mortar and pestle and sieved using Taylor series screens. The average

size of the sieved particles used in the experiments was <325µm. The sieved biomass

was further oven dried at 150°C to remove any residual moisture content and then

stored in a sealed polythene bag to prevent moisture absorption from the environment.

The resulting biomass, designated as DKSC, was used as the sorbent in all experiments.

The steps involved in the pre-treatment of DKSC biomass are shown in Fig. 3.1.

31

Figure 3.1: Procedure for the preparation of biosorbent.

3.2.2. Characterisation of pre-treated biomass

The DKSC was characterised by standard NREL-LAP (National renewable energy

laboratory – laboratory analytical procedure) methods for estimating its

physicochemical properties, ultimate analysis (CHNX), Fourier transform infrared

spectroscopy (FTIR) for determining its surface functional groups and Scanning

Electron Microscopy (SEM) for determining its surface morphology at different stages in

the removal and recovery of radionuclides.

(a) Physico-chemical properties: The physicochemical properties of DKSC namely

pH, moisture content, bulk density, surface area were determined by adapting NREL

standard LAP’s for biomass (Radha kumari, 2014) and are discussed below.

pH: To determine the pH of the sorbent, 1g of DKSC was placed into a 250 ml

Erlenmeyer flask, and 100 ml of distilled water was added. The mixture was boiled for 5

minutes. The solution was then diluted to 200 ml using distilled water and allowed to

32

cool to room temperature. The pH of this solution was measured, and the readings were

noted.

Moisture content: 5g of DKSC was weighed into a Petri dish and oven dried for

5hours at 105oC. The sample was then removed from the oven and placed in a

desiccator immediately to avert any moisture uptake from the atmosphere. This

procedure was repeated several times until a constant weight of the sorbent was

achieved. The moisture content of the sorbent at each trial was determined using the

equation,

% 𝑀𝑜𝑖𝑠𝑡𝑢𝑟𝑒 𝑐𝑜𝑛𝑡𝑒𝑛𝑡 = 𝑤2−𝑤3𝑤2−𝑤1

𝑋 100 (3.1)

where w1 is the weight of Petri dish, w2 is the weight of Petri dish plus sorbent before

drying, and w3 is the weight of Petri dish plus sorbent after drying.

Bulk Density: An empty 10 ml measuring cylinder was dried and weighed

initially. The measuring cylinder was then filled with DKSC up to the top and weighed

again. The difference in the weights of cylinder +DKSC and empty cylinder provided the

weight of DKSC. The bulk density of DKSC was determined using the equation

Bulk Density = 𝑤2−𝑤1𝑣

(3.2)

where w1 is the weight of the empty measuring cylinder, w2is the weight of the cylinder

plus sorbent and v is the volume of the measuring cylinder.

Specific surface area: Sears’ method (Jr, 1956) was used for the determination of

the surface area of DKSC. Half a gram of DKSC sample was placed in an Erlenmeyer flask

and acidified using 0.1M HCl solution until the pH was 3-3.5. Fifty ml of distilled water

and 10g of NaCl were added to the sample. The entire mixture was titrated with

33

standard 0.1M NaOH solution until pH increased to 4, and then to pH 9.0. The volume of

NaOH required to raise the pH from 4.0 to 9.0 was determined as V. The specific surface

area (S)was estimated from the equation,

S (m2/g) = 32V-25 (3.3)

pHpzc: The pHpzc of DKSC was evaluated by following the method described by

(Zolgharnein et al., 2013). Fifty ml of 0.01M KNO3 solution was placed in a

Erlenmeyerflask, and 0.1g of DKSC was added to it after adjusting the pH of the solution

to the desired value in the range of 2-11 using 0.1M HCl/NaOH solution. The mixture

was mildly agitated for 48h at the ambient condition to make sure equilibrium was

reached. The final pH of the solution was noted as pHf. The difference (∆𝑝𝐻) between

the initial and final pH values was calculated and plotted against initial pH (pHi). The x-

intercept of the linear curve is designated as point zero charge (𝑝𝐻𝑝𝑧𝑐).

(b) Elemental Analysis (CHNX):

Elemental analysis to determine the mass percentages of carbon, hydrogen, nitrogen,

hetero atoms (halogens, X) and oxygen of a sample based on the principle of ‘Dumans

Method,' which comprises the complete and instantaneous oxidation of the sample by

‘flash combustion.' This analysis gives information that helps in determining the

structure and purity of unknown /synthesized compounds.

Fig. 3.2 depicts the working principle and the major components of a typical CHNX

analyzer that includes units such as a sampler, a combustion/ignition chamber, a

packed column, a chromatographic column, and thermal conductivity detector (TCD).

The samples were freeze-dried, crushed, weighed and mixed with an oxidiser in a tin

capsule and the resulting mixture was combusted in an ignition chamber at 1000˚C. Tin

34

promotes the violent reaction (flash combustion) in a temporarily enriched oxygen

atmosphere. The combustion products were carried by a constant flow of carrier gas

and were passed through a glass packed column and then eluted into the

chromatographic column. A TCD detector was used to detect and measure the

concentrations of combustion product gases NO2, CO2, SO2, and H2O. The output signals,

which are proportional to the concentration of the individual components of the

mixture, were recorded. The chromatographic responses were calibrated against pre-

analysed standards and the CHNX elemental compositions were reported in weight

percentages.

Figure 3.2: Major components of a CHNX analyzer.

An Elementrovario MICRO cube model (made in Germany) CHNX analyser was used for

the determination of CHNX compositions in Karanja biomass (deoiled) according to the

procedure described above.

(c) FTIR Spectroscopic analysis

FTIR is a quantitative and qualitative analytical technique operating based on the

principle that most molecules absorb light in the infrared region of the electromagnetic

35

spectrum, which can be used to distinguish different organic and inorganic molecules

based on their peculiar absorption profile in the fingerprint region. This technique is

effective in identifying functional groups, side chains and cross-links involved in

different compounds and for characterising covalent bonding information.

The FTIR spectrum of DKSC was obtained following the KBr disk technique. The

biomass was ground into a fine powder and mixed with KBr (spectroscopic grade) in

the proportion 1 to 2% (w/w). The disk was pressed in a hydraulic press and used in

the measurement. The transmittance of the FTIR spectrum was observed over the range

400-4000 cm-1 using a Perkin Elmer Spectrum 100 FTIR spectrometer.

(d) SEM analysis:

SEM analysis reveals information about the external morphology (texture), chemical

composition (in conjunction with EDS), crystalline structure and orientation of

materials making up the sample. The technique is used to produce high-resolution

images and show spatial variations in chemical composition.

Figure 3.3: Schematic diagram of a typical SEM instrument.

36

The principle of SEM involves the scanning the surface of the sample using a

focused beam of electrons that interact with atoms at various depths within the sample,

further generating signals such as secondary and back-scattered electron which convey

information about the topography and composition of the sample surface. Fig. 3.3

displays the essential components of SEM including electron source (gun), a condenser

lens, deflection coils, final lens aperture and a secondary electron detector. An electron

beam emitted from the electron gun is focused by the condenser lens. It passes through

a pair of deflector plates in the electron column to the final lens, which deflects the

beam. When the electron beam interacts with the sample, the electrons lose energy by

repeated random scattering. The absorption of the beam current takes place within the

interaction volume of the sample which is detected by specialised detectors (e.g.,

secondary electron and back-scattered electron detectors plus energy dispersive x-ray

detectors to measure composition). An electronic amplifier amplifies the signals

generated by the detectors, which are used to create images of the distribution of

specimen current. The images are displayed as variations in brightness on a computer

monitor or as intensity versus energy spectrum showing characteristic x-rays.

A Hitachi S-3000N scanning electron microscope was employed for

characterising the surface morphology of the pre-treated Karanja biomass and metal-

loaded biomass. The sample preparation method was as follows. The biomass samples

were initially degreased, washed with solvents and dried thoroughly. The powder

sample was then compressed into small disks and mounted on carbon tape on a

specimen stub. The sample holder was turned upside down before analysis to ensure

the removal of loosely bound materials.

37

3.3. Preparation of thorium and zirconium stock solutions

A thorium stock solution of 1000 mg/L was prepared by dissolving exactly 2.4571 g of

Th(NO3)4.5H2Oin demineralized water and acidified with 1ml of concentrated HNO3to

prevent hydrolysis. The stock solution was prepared and stored in an airtight

polypropylene container.

A zirconium stock solution of 1000 mg/L was prepared by dissolving exactly

3.53g of ZrOCl2.8H2O in 1000ml of 0.1M hydrochloric acid. The stock solution was

prepared and stored in an airtight polypropylene container. The glassware used in

experiments were immersed in 10% (v/v) HNO3solution overnight and rinsed several

times with demineralised water before used in experiments.

3.4. Quantification of thorium and zirconium by UV/Vis

spectrophotometry

3.4.1. Working principle of UV/Vis spectrophotometry

Spectrophotometry is a technique that uses the absorbance of light by a substance at a

certain wavelength to determine the analyte concentration. UV/VIS uses light in the

ultra violet and visible spectral region. This technique is based on Beer-Lambert law

which states that the absorbance of the sample at a given wavelength is proportional to

the absorptivity of the substance (constant at each wavelength), the path length (the

distance the light travels through the sample) and the concentration of the absorbing

substance. It is expressed in the form the following equation:

𝐴 = 𝑎 𝑋 𝑏 𝑋 𝑐 (3.4)

38

where A is the absorbance of the sample, a is the absorptivity of the substance, b is the

path length, and c is the concentration of the substance.

The minimum elements required in a UV/Vis instrument are the following (Fig.

3.4); light source, usually a tungsten lamp for the visible region of the spectrum or a

deuterium lamp (D2) for ultraviolet wavelengths, a monochromator to produce a beam

of single radiation selected from a wide range of wavelengths via filters, and cuvettes

quartz or silica cells for holding the analyte to be measured and also to introduce the

samples into the light path, and detectors.

Figure 3.4: Schematic diagram showing the principles of UV-Vis

spectrophotometry.

3.4.2. Quantification of Thorium:

A spectrophotometric technique was carried out to estimate the total Th+4 metal ion

concentration in the sample aliquots. The analytical methodology is based on the

complex formation of thorium with xylenol orange developed by Mukherji (1966).

39

Calibration curve: A standard calibration curve was generated using the Thorium AAS

standard, which was diluted with demineralised water to obtain the desired thorium

concentrations in the range 2-15mg/L. The standards were then buffered with 10ml of

acetate buffer to maintain a constant pH. Five ml of xylenol orange reagent solution was

added to the thorium solution and allowed to stabilise for approximately 3 hours. The

absorbances of these standards were measured using the UV-Vis spectrophotometer at

575nm using 1cm cell quartz cuvettes calibrated against blank. The calibration plot for

thorium is shown in Fig. 3.5 (a). The correlation coefficient obtained for the calibration

curve was 0.9989.

The concentrations of thorium present in the samples from the experiments

were determined from this standard curve using the absorbance measured using the

UV-Vis spectrophotometer.

(a) Xylenol orange solution (10-3M): The reagent solution was prepared by

dissolving 0.7606g of xylenol orange tetra sodium salt dry powder in 50% ethanol

(v/v). The reagent was always prepared fresh.

(b) Sodium Acetate Buffer Solution: Sodium acetate buffer of pH = 6±0.2 was

prepared by mixing an appropriate portion of 0.01M glacial acetic acid with 0.01M

sodium acetate solution.

0.01M glacial acetic acid: It was prepared by diluting 0.3mL of glacial acetic acid

with 500ml of demineralised water.

0.01M sodium acetate: It was prepared by dissolving 4.1g of sodium acetate

anhydrous with 500ml of demineralised water.

40

3.4.3. Quantification of zirconium:

The concentration of Zr(IV) in the sample aliquots was quantified using a

spectrophotometric technique based on the reaction of zirconium with xylenol orange

as discussed by (Akkaya and Akkaya, 2013).

Calibration Curve: A standard calibration curve was generated using standard Zr

solutions in the concentration range of 0.05-2.5 mg/L. These were prepared by diluting

Zirconium AAS standards with demineralised water. The standard sample was mixed

with xylenol orange solution reagent in the ratio of 20:2(v/v)and allowed to stand for

approximately 30 minutes. Xylenol orange reacts slightly with zirconium to form a

complex that has the best absorbance at 535nm (Akthar et al., 2008). Thus, the

absorbance was measured at 535nm using 1cm cuvettes calibrated against reagent

blank. The calibration curve is shown in Fig. 3.5(b) and the correlation coefficient for

the calibration curve was 0.9682. The concentration of Zr(IV) present in the

experimental sample was measured using this standard calibration curve. The

calibration experiments for Zr were repeated 3 times and the trends in the calibration

curves were found to be similar. The percentage variation in the data was found to vary

± 2%.

(a) Xylenol Orange reagent solution (0.05%):The solution was prepared by dissolving

the xylenol orange tetra sodium salt dry powder in 0.6N hydrochloric acid. The reagent

was always prepared fresh.

41

Figure 3.5: Calibration curves for Th(IV) and Zr(IV) quantification using UV-Vis

Spectrophotometer; (a) thorium absorbance at 575nm and (b) zirconium absorbance at

535nm.

3.5. Biosorption and desorption experimental procedures

3.5.1. Biosorption experiments for the removal of radionuclides:

All the experiments were conducted in batch mode and the experimental procedures

were established from the thorough understanding of the preliminary studies.

Equilibrium studies were performed at 25˚C in 250 ml Erlenmeyer flasks using a

shaking incubator operating at 200rpm. The predetermined concentrations in metal

solutions (mg/L) of required volume were prepared by serial dilution of a metal stock

solution with demineralised water. The pH of the metal solutions was achieved by

adding the necessary amounts of 0.1M HCl/NaOH solution. The pH of the aqueous

solution was measured to ensure a consistent pH value during the entire experimental

run. This is because the pH values used in the experiments were chosen according to the

experimental design. A known weight of DKSC was added to the metal solution, and the

flasks were agitated for a fixed time until equilibrium was attained. The solid-liquid

y = 0.143x + 0.054 R² = 0.996

0

0.5

1

1.5

2

2.5

3

0 2 4 6 8 10 12 14 16 18 20

Abso

rbna

ce r

eadi

ng

Concentration (mg/L)

(a)

y = 0.3008x - 0.0416 R² = 0.9639

0

0.2

0.4

0.6

0.8

1

0 0.5 1 1.5 2 2.5 3

Abso

rban

ce r

eadi

ng

Concentration (mg/L)

(b)

42

mixture was separated after equilibrium by filtration using Whatman filter paper. The

filtrate was then analysed to determine the metal concentration (Th/Zr) using the

spectrophotometric methods discussed above (Section: 3.4).

For all the biosorption experiments, only the initial pH of the metal solution was

recorded because there were negligible changes in the pH values of the solution during

the experiment as can be seen from the data presented in Fig 3.6.

Figure 3.6: Effect of contact time on solution (Th/Zr) pH during biosorption process.

The kinetics and thermodynamic studies were performed in a four-necked 250

ml jacketed reactor equipped with a thermostat for temperature control and overhead

motor for stirring. Vigorous stirring was provided to the solid-liquid mixture using a

PTFE (ploy tetra flouro ethylene) anchor type impeller to overcome the external mass

transfer resistance. The kinetic studies were carried out at 25˚C by varying the initial

metal concentration (mg/L) of thorium and zirconium in the feed (working volume

200ml) using a predetermined DKSC loading for 5 hours. Samples were withdrawn from

the reactor vessel at regular time intervals and analysed for the metal concentration.

The procedure followed for the thermodynamic studies was similar to that used for the

kinetic studies except that the temperature was different for thermodynamic studies

2.9

3.2

3.5

3.8

4.1

0 30 60 90 120 150 180 210 240

pH o

f met

al (T

h/Zr

) sol

utio

n

Time(min)

25 mg/L50 mg/L100 mg/L

43

(15 to 45˚C). The experimental setup used for the kinetic and thermodynamic studies

are shown in Fig. 3.7.

Figure 3.7: Experimental setup used for kinetic and thermodynamic studies in

biosorption and desorption studies.

The experimental results were used to determine metal uptake capacity or

biosorption capacity (metal ions sorbed per gram of biosorbent) (qe, mg/g), the bio-

removal efficiency (𝑅, %) and the distribution coefficient (Kd, L/g) using the following

equations

qe = (Ci−Ce)Vm

(3.5)

R(%) = Ci − Ce

Ci x 100 (3.6)

Kd = Ci−CeCe

x V m

(3.7)

Overhead motor

Stirrer

Metal solution and biomass

Jacketed reactor

Stand for support

Syringe for sample collection

44

where Ci and Ce are the initial and the equilibrium concentrations of the metal solution

before and after biosorption in mg/L, V is the working volume of metal solution in L,

and m is the mass of DKSC in g.

3.5.2. Desorption experiments for the recovery of radionuclides from loaded

biomass:

Desorption experiments were performed in batch mode using metal (Th/Zr)-loaded

DKSC obtained from the biosorption experiments. The loaded DKSC was vacuum

filtered on Whatman filter paper, washed multiples times with distilled water over the

filter paper to remove loosely bound metal ions and then air dried overnight and

secured in an airtight bag.

The equilibrium studies for desorption was performed in Erlenmeyer flasks. The

volumes of eluant and loaded sorbent were chosen to obtain a fixed L/S ratio. The

eluant and loaded DKSC mixture was stirred at 25°C in a shaking incubator operating at

200rpm for six hours. The desorbed DKSC was filtered from the eluant using Whatman

filter paper, and the eluantwas subjected to spectrophotometric analysis for the

quantification of metal ions present in the eluant.

Kinetics and thermodynamic experiments performed for desorption studies

were similar to those for biosorption studies. The desorption experimental results were

used to determine the desorption capacity, qdes (mg/g), and the desorption efficiency,

D%, using the following equations:

𝑞𝑑𝑒𝑠 = 𝐶𝑑𝑒𝑠𝑚𝑋 𝑉 (3.8)

𝐷% = 𝑞𝑏𝑖𝑜−𝑞𝑑𝑒𝑠𝑞𝑏𝑖𝑜

𝑋 100 (3.9)

45

where Cdes is the concentration of the metal ions in the eluant in mg/L, V is the volume

of eluant in L, m is the mass of loaded sorbent in g, and qbio is the biosorption capacity of

DKSC in mg/g (obtained from the biosorption studies).

3.6. Design of Experiments (DOE) technique for the process

optimisation

In an experimental study involving a number of process parameters, it is important to

understand the influence of process parameters and their interaction on process performance

to determine an optimum set of parameters that ensure the desired outputs. The classical style

of trial and error approach to determine the optimum set of process parameters has drawbacks

especially when the number of possible process parameters is high because it fails to

elucidate the effects of interaction between the parameters. On the other hand, the design of

experiments (DOE) technique helps to determine the minimum number of experiments that

consists of a possible parameter combination. DOE also suggests parameter domains where

the process offers the most benefit. DOE is a series of runs/tests that involve purposeful

changes to input variables and observing the change in responses. The main criteria that need

to be considered while picking an appropriate DOE are:(i) it should identify the number of

control factors with their respective levels, (ii) it should determine the least possible number

of runs that should be performed and (iii) it should minimise the impact of cost, time, and use

of chemicals.DOE includes various techniques/methods that can be applied for successful

optimisation of the process to attain maximum benefit. They include: (a) Factorial designs,

(b) Response Surface designs(c) Mixture designs and (d) Taguchi designs.

In the present research, Taguchi and Response Surface (RSM) designs were

adapted to optimise the thorium and zirconium biosorption process parameters.

46

3.6.1. Taguchi robust design

Dr. Genichi Taguchi, a scientist in Electronic Control Laboratory in Japan, carried out a

major research and proposed the theory of robust design in DOE, commonly known as

Taguchi method in early 1980's in the USA. The Taguchi approach is a new form of DOE

with unique application principles and experimental strategies that can be inexpensive

in optimisation. It attempts to improve the performance quality, and it is achieved via

reducing variation in a process through the robust design of experiments (Kamaruddin

et al., 2010).

Taguchi design contains specially constructed tables called orthogonal arrays

(OA), which is a combination of control and noise factors selected by a number of

factors (variables) and levels (states). With OA, the design is balanced with all the

factors and levels weighing equally. Each factor can be evaluatedindependently, and the

effect of one factor does not influence the estimation of another factor. The selection of

an appropriate orthogonal array depends on the total degrees of freedom of the

parameters involved in the process study (Daneshvar, 2007).

Unlike full factorial combinations, Taguchi tests a certain pair of combinations

affecting the process and the levels as the particular parameter space is varied. This

allows the collection of the necessary data to determine which factors affect the product

quality the most with minimum experimental trials, thus saving time, cost, labour and

resources. This method is best suited for an intermediate number of variables (3 to 50),

few interactions between variables, and only when a few variables contribute

significantly. The experimental data collected is transformed into a signal-to-noise

(S/N) ratio which is a measure of response variations and is also a performance

parameter to measure the sensitivity of quality characteristic deviating from the

47

measured values. It is the log transformation of the mean square deviation of the

desired response, where the signal (S) is the desirable effect (mean), and the noise (N)

is the undesirable effect (signal disturbance). The data from the arrays can be analysed

by plotting the data and performing visual analysis, analysis of variance

(ANOVA),Fisher's exact test, or Chi-squared test to test significance (Daneshvar et al.,

2007). The general steps involved in Taguchi approach are shown in Fig. 3.8.

Figure 3.8: Algorithm for Taguchi approach.

Usually, there are three categories of quality characteristic in the analysis of the S/N

ratio, i.e lower-the-better, higher-the-better, and nominal-the-better. An appropriate

criterion for the S/N ratio must be chosen depending on the goal for the optimisation of

the process parameters.

48

(1) Lower-is-better: If the aim is to minimise the performance, the value of the

S’’/N ratio should be low.

𝑆′′𝑁𝑖

= −10 𝑙𝑜𝑔10 �1𝑛𝑖∑ 𝑌𝑖2𝑛𝑖=1 � (3.8)

(2) Higher-is-better: If the goal is to maximise the performance, the value of

the S’/N ratio should be high.

𝑆′𝑁𝑖

= −10 𝑙𝑜𝑔10 �1𝑛𝑖∑ 1

𝑌𝑖2

𝑛𝑖=1 � (3.9)

(3) Nominal-is-better: If the goal is to achieve a predetermined S/N, then the

value of S’’’/N need to be a target value.

𝑆′′′𝑁𝑖

= −10 𝑙𝑜𝑔10 �1𝑛𝑖∑ [𝑆𝑞𝑢𝑎𝑟𝑒 𝑜𝑓 𝑚𝑒𝑎𝑛

𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒]𝑖𝑛

𝑖=1 � (3.10)

where 𝑌𝑖is the response variable for n observations.

3.6.2. RSM - Box-Bohnken experimental design:

Response Surface Methodology is a proficient statistical tool involving a set of

mathematical and a statistical technique used for modeling and analysis of problems

and is mainly used by industrial engineers to optimise process parameters as it offers a

fewer number of runs at a specific position of design points that can reduce time, cost

and resources. It is the most effective and powerful experimental design when

considered in comparison to other response surface designs like central composite,

Doehlert matrix and 3-level full factorial designs(Sharma et al., 2009, Islam et al., 2009,

Douglas and Montgomery)). RSM involves a stepwise procedure for optimising a

process via statistical evaluation of the designed experiments, estimation of the

coefficients in a mathematical model, and testing the adequacy of the model. The

detailed steps involved in this approach are shown in Fig. 3.9.

49

Figure 3.9: Sequential steps required for RSM

The response Y is influenced by many process variables and a relationship

between them is established by the RSM technique. Analysis of a response, Y which is

dependent on process variables X1, X2,. ..,Xn, is done using the correlation:

Y = f (X1,X2, · · ·Xn) ± ε (3.11)

where f is the real response function (its format isunknown), and ε is the error which

describes the differentiation that can be integrated by the function f.

Box–Behnken is a spherical design consisting of central and middle points at the edges

of the cube circumscribed on the sphere. The number of experiments required in this

50

design can be decided according to equation 3.12 (Mourabet et al., 2012, Aravind

Kumar, 2008)).

N= 2k (k-1) + cp (3.12)

where k is the independent factor and cp is the replicate number of the central point. In

a process involving three independent variables (X1, X2, X3), the relationship between

response (Y) and the variables can be approximated by a quadratic second order

polynomial;

Y = β0 + β1X1 + β2X2 + β3X3 + β12X1X2 + β13X1X3 + β23X2X3 + β11X21 +

β22X22 + β33X23 (3.13)

where Y is the predicted response, β0 is the model coefficient at the centre point, β1, β2,

β3 are the linear coefficients, β12, β13 and β23 are the cross-product coefficients, β11, β22

and β33 are the quadratic coefficients. Multiple regression analysis can be done to obtain

the values of coefficients, and the equation can be used to predict the response. The

goodness-of-fit of the quadratic model can be determined by the coefficient of

determination (R2) (Akar et al., 2014, G. Annadurai, 1998).

3.6.3. Percentage contributions :

The total percentage contributions (TPC) for all the possible first-order, interaction and

quadratic terms were obtained by the method adopted by Yetilmezsoy (2009) using the

following equations.

𝑇𝑃𝐶𝑖 = ∑ 𝑆𝑆𝑖𝑛𝑖=1

∑ ∑ 𝑆𝑆𝑖+𝑆𝑆𝑖𝑖+𝑆𝑆𝑖𝑗𝑛𝑗=1

𝑛𝑖=1

× 100 (3.14)

𝑇𝑃𝐶𝑖𝑗 =∑ ∑ 𝑆𝑆𝑖𝑗𝑛

𝑗=1𝑛𝑖=1


𝑛𝑖=1

× 100 (3.15)

𝑇𝑃𝐶𝑖𝑖 =∑ ∑ 𝑆𝑆𝑖𝑖𝑛

𝑗=1𝑛𝑖=1


𝑛𝑖=1

× 100 (3.16)

51

where 𝑇𝑃𝐶𝑖, 𝑇𝑃𝐶𝑖𝑗and 𝑇𝑃𝐶𝑖𝑖 are the total percentage contributions of the first-order,

interaction and quadratic terms, respectively. Similarly, 𝑆𝑆𝑖, 𝑆𝑆𝑖𝑗, 𝑆𝑆𝑖𝑖 are the computed

sum of squares for the first-order, interaction and quadratic terms, respectively.

3.6.4. Desirability approach for multi-variate optimisation :

The desirability method is an established technique for the simultaneous determination

of the optimum settings of input variables that can determine the optimum performance

levels for one or more responses. Depending on whether a particular response Yi is to

be maximised, minimised or assigned a target value, different desirability functions di

(Yi) are used (G. Derringer, 1980).

As an example, let Li, Ui and Ti be the lower, upper and target values, respectively, that

are desired for response Yi, with Li ≤ Yi ≤ Ui. If the response is the “target is the best”

kind, then its desirability function is given by equation (3.14),

𝑑𝑖 = (𝑌𝑖−𝐿𝑖𝑇𝑖−𝐿𝑖

)𝑝 if Li ≤ Yi ≤ Ti

𝑑𝑖 = (𝑌𝑖−𝑈𝑖𝑇𝑖−𝑈𝑖

)𝑞 if Li ≤ Yi ≤ Ti (3.17)

di = 1 if Yi = Ti

di = 0 if Yi ≤ Li or Yi = Li

where the exponents p and q determine how important it is to hit the target value.

If a response is to be maximised instead, the individual desirability is defined as shown

in equation (3.15),

di = 0 if Yi ≤ Li

𝑑𝑖 = (𝑌𝑖−𝐿𝑖𝑇𝑖−𝐿𝑖

)𝑝 if Li ≤ Yi ≤ Ti (3.18)

di = 1 if Yi ≥ Ti

Finally, if a response is to be minimised, the individual desirability (di) is calculated

according to equation (3.19),

52

di = 1 if Yi ≤ Ti

𝑑𝑖 = (𝑌𝑖−𝑈𝑖𝑇𝑖−𝑈𝑖

)𝑞 if Ti ≤ Yi ≤ Ui (3.19)

di = 0 if Yi ≥ Ui

In a multi-response circumstance, the ideal case is that the all responses’ desirability

value is equal to 1 and the whole response’s desirability value also equals 1. If any

response cannot achieve the requirement, the ideal case of the whole response cannot

be achieved and is considered as the unacceptable case. Moreover, if the desirability

value of any response equals to 0, the whole response will also be considered to be the

unacceptable case. To complete the requirement, the whole response’s desirability

value can take the geometric average of all responses’ desirability value, i.e.

D = (d1 x d2 x d3 x ……. x dn )1/n = (∏ 𝑑𝑖)𝑛𝑖=1

1/𝑛 (3.20)

where di represents the desirability value of the ith response, and n represents the

number of responses in the measure, i=1, .... n. In other words, D equals 1 when all

responses achieve the target, and the D equals 0 when any one response cannot achieve

the requirement.

It can be extended to:

D = (𝑑1𝛼1 𝑥 𝑑2𝛼2 𝑥… … 𝑥𝑑𝑛1/𝑛)1/𝑛, (3.21)

where 0 ≤ 𝛼𝑖 ≤ 1, (i = 1,2,3,….n), 𝛼1 + 𝛼2 + ⋯… . +𝛼𝑛 = 1

where di indicates the desirability of the different responses, Yi (I = 1,2,3,…n) and αi

represent the importance of responses. So, maximum overall desirability function D

depends on the αi (importance) value.

53

Chapter4 Thorium biosorption and

optimisation studies via

Taguchi and desirability

approach

54

Summary

The research presented in this chapter discusses the sequestration of thorium from

aqueous solutions by applying the biosorption method as a separation technique. An agro-

industrial waste biomass namely deoiled Karanja biomass was employed as the sorbent in

the biosorption studies for the removal of thorium metal ions. The effects of process

variables including initial metal concentration, pH of the feed solution and DKSC loading

were examined. Design parameters were optimised to obtain the maximum biosorption

capacity and bio-removal efficiency using a fractional factorial design of experiments and

a desirability approach for multivariate optimisation. A Taguchi robust design, L16 (43)

orthogonal array was used for the optimisation of process parameters. Using the optimum

parameter combination obtained from the Taguchi method gave a maximum efficiency of

91.97% at an initial Th concentration of 130 mg/L, pH of 5.0 and a DKSC loading of 0.25

g/L.

4.1. Introduction

Thorium is a naturally existing radioactive nuclide with nuclear significance and is an

active gamma emitting by-product of nuclear reactor operations. The speciation of Th

from the nuclear discharge is a significant concern in nuclear waste management and

also for environmental pollution control. The methods employed in dealing with this

radionuclide from aqueous and industrial waste streams using different kinds of

biomass were discussed in Chapter 2.

4.2. Experimental Investigations

Materials and experimental methodologies employed to carry out the present work

were described in Chapter 3 (Materials and Methodology).

55

4.2.1. Preliminary studies:

The parameters such as contact time, mixing speed, pH, and DKSC loading were

investigated in preliminary studies. Variables were tested in a univariate fashion (i.e.,

varying one parameter at a time while keeping remaining constant) and all experiments

were conducted in batch mode.

4.2.2. Taguchi L16 (43) OA design:

A Taguchi L16 orthogonal array (OA) comprising of three factors (initial Th(IV)

concentration, initial pH and DKSC loading) with four levels each (Table 4.1) was

employed to study the effect of process variables towards biosorption capacity(qe) and

bio-removal efficiency (R%) in the thorium biosorption studies.MINITAB17 Statistical

software (free trial) was utilised for the generation of the experimental design matrix

with the selected factors at their respective levels. The L16OA suggested 16 runs were

requiredin the experimental design for the selected factors with their corresponding

levels as presented in Table 4.2 along with the results obtained.

Table 4.1: Factors and levels considered in the Taguchi robust design.

Factors/Levels 1 2 3 4 Initial Th concentration (mg/L) 15 40 85 130

Initial pH 2 3 4 5

DKSC loading (g/L) 0.25 0.50 0.75 1.00

4.3. Results and discussions

4.3.1. Preliminary investigations

The shaking speed and equilibrium time were identified as the principal process

variables that were required to be fixed for the biosorption studies. Thus, those

variables were investigated in the initial test work. Visual observations led to the

56

Table 4.2: Taguchi L16 OA design results for the biosorption of thorium studies.

Exp.run order

Ci

(mg/L) pH

DKSC loading

(g/L)

Ce

(mg/L) 𝐪𝐞

(mg/g) 𝐒 𝐍⁄ ratio

for qe 𝐑%

𝐒 𝐍⁄ ratio for R%

𝐃

1 15 2 0.5 1.19 27.03 28.64 92.07 39.28 0.31 2 15 3 1.0 1.54 13.45 22.57 89.74 39.06 0.21 3 15 4 1.5 0.15 9.89 19.90 99.92 40.00 0.19 4 15 5 2.0 0.35 7.41 17.39 99.93 40.00 0.17 5 40 2 1.0 39.86 0.14 -17.12 00.35 -9.13 0.00 6 40 3 0.5 29.16 21.46 26.63 27.10 28.66 0.15 7 40 4 2.0 0.86 19.98 26.01 99.95 40.00 0.28 8 40 5 1.5 7.34 21.67 26.72 81.64 38.24 0.26 9 85 2 1.5 60.84 16.11 24.14 28.42 29.07 0.13

10 85 3 2.0 35.17 24.86 27.91 58.62 35.36 0.24 11 85 4 0.5 7.48 154.00 43.74 91.20 39.20 0.73 12 85 5 1.0 1.61 78.82 37.93 98.11 39.83 0.54 13 130 2 2.0 5.03 62.48 35.91 96.13 39.66 0.48 14 130 3 1.5 5.24 83.17 38.40 95.97 39.64 0.55 15 130 4 1.0 0.74 130.00 42.28 99.94 40.00 0.71

16 130 5 0.5 0.24 260.00 48.30 99.97 40.00 1.00

Ci– initial Th(IV) concentration, Ce – equilibrium Th(IV) concentration, D-Desirability

conclusion that, at lower mixing speeds, the biosorption capacity of DKSC was at a

minimum due to an inefficient dispersion of sorbent particles in the working solution

that led to agglomeration of sorbent particles at the bottom of the reactor vessel. It was

also found that a mixing speed of 200 rpm was sufficient for the availability of all

surface binding sites in the sorption process. The biosorption capacity reached a

maximum value at this mixing speed, and therefore the agitation speed was chosen as

200 rpm for all further batch biosorption studies.

57

Figure 4.1: Effect of contact time towards bio-removal efficiency (R%) and initial Th

concentration in feed (Ci, mg/L).

The effect of shaking time (i.e. contact time) on thorium biosorption onto DKSC

was examined up to a maximum of 5 hours using solution with 28 mg/L of initial Th(IV)

concentration as the feed with an initial pH of 6 and DKSC loading of 0.1g for 100 ml.

The results obtained are displayed in Fig. 4.1. As can be seen in the figure, the initial

Th(IV) concentration (Ci mg/L) in the feed decreases with increasing time indicating the

biosorptive capability of DKSC in adsorbing thorium ions. Thorium uptake was very

rapid in the first 30minutes, and it led to 82% removal efficiency. Equilibrium was

attained within 180 minutes. Hence, the equilibrium time was fixed as 180 minutes for

all further biosorption experiments. The samples were collected at fixed time intervals,

and the time intervals were chosen on the basis of preliminary experiments. The trends

in the graphs plotted using the results from the preliminary experiments were similar

to those obtained using actual experiments. Thus, more data within the 0-30 minutes

were not deemed to be required.

0

5

10

15

20

25

30

0

20

40

60

80

100

120

0 60 120 180 240 300 360

Ci m

g/L R%

Contact time, t minutes

R% Th Concen in feed

58

Literature suggested that precipitation of thorium takes place when pH >6

(Sayana et al., 2016). Hence, the pH values were varied from 2 to 6. Precipitation of

thorium was also examined in this pH range, and it was found that there was no

precipitation up to pH of 6.0. However, thorium was precipitated at higher

concentrations when the pH was equal to 6.

4.3.2. Multivariate optimisation of Th(IV) biosorption process using the Taguchi

robust design with desirability approach

(a) Statistical analysis of Taguchi L16OA design

The experiments were performed according to the design (Table 4.2) as specified in the

section 3.5.1 and the data collected was converted into biosorption capacity (qe) and

bio-removal efficiency (R%) according to equations 3.5 and 3.6, respectively. The

results calculated were treated for statistical significance by employing MINITAB17

statistical software (trial version). The qe and R% values that were calculatedwere

transformed into 𝑆 𝑁 ⁄ ratios (larger-is-better) according to equation 3.9 and are

presented in Table 4.2.

Table 4.3: Response table for signal-to-noise ratios(𝑆 𝑁) ⁄ - Larger is better.

Levels/Factors

Initial Th(IV) concentration

Initial pH DKSC loading

qe R% qe R% qe R%

1 22.13 39.59 17.89 24.72 36.83 36.79 2 15.56 24.44 28.88 35.68 21.42 27.44 3 33.43 35.87 32.98 39.80 27.29 36.74 4 41.22 39.82 32.59 39.52 26.81 38.75

∆𝒎𝒂𝒙−𝒎𝒊𝒏 25.66 15.38 15.09 15.08 15.41 11.31 Rank (𝒒𝒆) 1 3 2

Rank (𝑹%) 1 2 3

∆𝑚𝑎𝑥−𝑚𝑖𝑛:Rangeis the difference between maximum and minimum levels of factors

59

Response tables for the two responses (qe and R%) obtained from signal-to-

noise ratios (𝑆 𝑁) ⁄ are shown in Table 4.3. From the observation of the rankings

obtained for each variable, it is evident that initial Th(IV)concentration is the most

influential variable for the two responses R% and 𝑞𝑒 in the biosorption process for

thorium, whereas pH and DKSC loading were the least influential factors. The same data

has been shown in Fig. 4.2 in terms of percentage contribution from each parameter.

Thus, the contribution of initial Th(IV) concentration towards the responses 𝑞𝑒

and 𝑅% has the greatest effect.

Figure 4.2: Percentage contributions of process variables towards responses 𝑞𝑒and R%.

ANOVA (Analysis of variance) was performed for the experimental data to determine

the effects of the various factors towards the responses. Results are presented in Table

4.4. An F-test was carried out for experimental results, and the results were compared

withcritical values. As is evident from Table 4.4., the initial metal concentration has the

largest sum of squares indicating it is the most influential operational parameter. From

the calculated F-values, it is clear that no single parameter has a statistical significance

at 95% confidence level.

46.31

17.51 14.49

40.82 38.99

20.19

0

10

20

30

40

50

60

70

80

Initial concentration pH loading

qe R%

60

Table 4.4: ANOVA table for qe and R% in 𝐿16OA design.

Factors SS DOF MS Fa Fcrb PC

For𝒒𝒆

Initial Th concentration 1574 3 524 4.27 9.28 46.31

Initial pH 595 3 198 1.61 4.76 17.51

DKSC loading 492 3 164 1.34 3.86 14.49

Residual 737 6 122 ---- ---- ----

Total 3399 15 ---- ---- ---- ----

For R%

Initial Th concentration 626 3 208 1.68 9.28 40.82

Initial pH 598 3 199 1.61 4.76 38.99

DKSC loading 309 3 103 0.83 3.86 20.19

Residual 743 6 123 ---- ---- ----

Total 2277 15 ---- ---- ---- ---- aFischer’s ratio; eF0.05(ν1, ν2) where ν1 is the degree of freedomand ν2 = (a*n –a),

a is factor number, and n is the number of levels

Fig. 4.3 displays the main effect plots of the 𝑆 𝑁 ⁄ ratios for the responses 𝑞𝑒 and

𝑅% obtained from the Th(IV) biosorption studies. The higher response represents the

best level of each variable and can be interpreted as an optimised value for that

particular process variable.

Effect of Initial Th(IV) concentration; The initial Th(IV) concentration shows a

critical effect towards 𝑞𝑒 and R% as it explains the metal uptake mechanism. As

perceived from Fig. 4.3.(a) and (b), the biosorption capacity (𝑞𝑒) and bioremoval

efficiency (R%) initially decreased with an increase in Th(IV) concentration from 15 to

40 mg/L and then increased as initial Th(IV) concentration was increased from 40 to

130 mg/L. The decrease in qe and R% with respect to initial Th(IV) concentration may

be due to the formation of a series of polynuclear complexes of thorium such as

𝑇ℎ2(𝑂𝐻)26+, 𝑇ℎ6(𝑂𝐻)159+ and 𝑇ℎ2(𝑂𝐻)7+ with high oxidation numbers along with the

anions in the solution. The uptake of these ions onto the sorbent is difficult.

61

Also, an increase in thorium concentration results in the increasing

hydrolyzation among thorium ions which are unable to reach the adsorption sites of the

sorbent (Yusan et al., 2012, Gok and Aytas, 2013, M. A. A. Aslani, 2001). The further

increase in thorium concentration led to a rising in qe and R% values since the initial

sorbate concentration increases the concentration gradient which in turn increases the

main driving force that overcomes all mass transfer resistances between the sorbent-

sorbate systems. The parameter qe increased with respect to the thorium concentration

(40 to 130 mg/L) due to the enhancement of electrostatic interactions (relative to

covalent interactions) between thorium and DKSC, whereas the R% parameter

increased due to the attainment of process equilibrium (Anirudhan et al., 2010, Bhalara

et al., 2014, Yusan et al., 2012, Kütahyalı and Eral, 2010)). Hence, qe and R% had higher

response values of 260 mg/g and 99.97%, respectively for an initial thorium

concentration of 130 mg/L. The values obtained here are the highest compared to those

achieved with many other adsorbents reported in the literature (Akkaya et al., 2013,

Gok et al., 2013, Gok et al., 2011, Ceren et al.,2010,Innoue et al., 2006). Thus, 130 mg/L

can be regarded as an optimum value for initial Th (IV) concentration for the present

sorbate-sorbent system.

Effect of initial pH; The pH is one of the important factors that influence the

biosorption of radionuclides onto DKSC. It affects the degree of ionisation, surface

charge, speciation and precipitation of metal ions. It is well known that surface charge

of the sorbent can be modified by changing the pH of the solution, and the chemical

species in the solution depends on the pH (Sayana et al., 2016). From Fig. 4.3 (a) and (b),

it can be seen that 𝑞𝑒and R% parameters both increased as the pH was increased from

2 to 4 and the maximum biosorption occurred at pH 4 (with nearly 100% of thorium

62

adsorbed onto DKSC). The increase can be explained based on the state at which the

thorium ion exists in aqueous solutions and is dependent on pH.

Figure 4.3: Main effects of the major variables (by 𝑆 𝑁 ⁄ ratios) on (a) 𝑞𝑒and (b) 𝑅%.

In acidic solutions, especially when the pH is < 3, thorium is present in +4

oxidation state as highly soluble species (Fig 4.4). Hydroxonium ions (𝐻3𝑂+) present in

strongly acidic solutions (2<pH<4) may compete with the thorium cations for the

sorption sites and hinder the sorption process. When the pH is around 4, 1:1 and 1:2

63

positively charged thorium acetate complexes [ThCH3COO]3+ and [Th(CH3COO)2]2+

appear as characteristic ions present in the solution (Yusan et al., 2012, Aslani et al.,

2001).

Figure 4.4: Thorium species distribution diagram.

The FTIR results revealed that DKSC contains carboxyl, hydroxyl, amine as major

functional groups and the pHpzc of DKSC was determined as 6.2 (Fig. 7.1). The increased

biosorption of Th(IV) in the pH range of 4-5 may therefore be caused by the ion-

exchange reactions of thorium ions with carboxyl groups due to deprotonation of these

functional groups at the surface sites that results in the increase of negative charge. The

exchange reactions involved can be shown as follows (Gok and Aytas, 2013, Ding et al.,

2014),

COOH + Th(𝑂𝐻)3+ → [(𝐶𝑂𝑂)𝑇ℎ(𝑂𝐻)3] + 𝐻+

𝑛 𝐶𝑂𝑂𝐻 + 𝑇ℎ(𝑂𝐻)3+ → [(𝐶𝑂𝑂)𝑛𝑇ℎ(𝑂𝐻)3](3−𝑛)+ + 𝑛 𝐻+ (n=1, 2, 3) (4.1)

𝑛𝐶𝑂𝑂𝐻 + 𝑇ℎ(𝑂𝐻)22+ → [(𝐶𝑂𝑂)𝑛𝑇ℎ(𝑂𝐻)2](2−𝑛)+ + 𝑛 𝐻+ (n=1,2)

Effect of DKSC loading; The DKSC loading determines the sorbate-sorbent

equilibrium and also the number of binding sites available for biosorption. Fig. 4.3(b)

depicts the plot of DKSC loading against 𝑅%, indicating the presence of surplus amounts

64

of unoccupied sites with the increase in DKSC loading (Anirudhan et al., 2010).The

results of the present study are in agreement with those of similar studies reported in

the literature (Akkaya and Akkaya, 2013, Yusan et al., 2012).

(b) Multivariate optimisation with desirability approach

The overall desirability values of each response were calculated by considering

equation 3.20 (Chapter 3) to verify whether the response is acceptable or not. The

independent desirability of each response (𝑞𝑒and 𝑅%) was combined to determine the

overall desirability and is reported in Table 4.2. It was observed that the combination

212 (Exp. No 5) shows an overall desirability of zero. Thus a 𝑆 𝑁⁄ ratio cannot be

calculated for this case. Therefore, the optimisation of the combination was carried out

based on the analysis by means of overall desirability (Nandi et al., 2010). The factors’

combination 4-4-1(Exp. No 16) reached an overall desirability value of 1 giving rise to

the conclusion that optimum process variables occur at 130 mg/L of initial Th

concentration, pH of 5 and a DKSC loading of 0.25 g/L. Thus, the maximum values

obtained at the optimum conditions using the desirability approach are 260 mg/g for qe

and 99.97% for R%.

4.3.3. Equilibrium studies and adsorption isotherm modeling

Equilibrium studies were performed to measure the capacity of DKSC in the biosorption

of thorium from aqueous streams. The results obtained are presented in Figure 4.4. The

results show that qe increased considerably with Th(IV) equilibrium concentration(Ce)

due to the reason that the original Th concentration tends to increase the interactions

among DKSC and thorium ions. However, qe attained almost a constant value with

increasing initial concentration due to the saturation of the sorption sites on DKSC (Aly

et al., 2013). The distribution coefficient (Kd) is the ratio of the equilibrium

65

concentration of meal ions in solid to that in the aqueous phase. Fig. 4.5 displays the Kd

as a function of Ce for Th(IV) biosorption. High values of Kd is an excellent feature for

the sorbent, and the Kd values obtained for DKSC were13.05 L/g for 𝐶𝑒of 1.78 mg/L

which decreased to 0.2 L/g for 𝐶𝑒of 585mg/L using 0.667 g/L of DKSC. A similar trend

in Kd was observed as a function of 𝐶𝑒by (Akhtar et al., 2008).

Figure 4.5: qe and Kd as a function of𝐶𝑒 .

Also, the equilibrium data were analysed using linear forms of adsorption

isotherms namely, Langmuir and Freundlich models (Section 2.2.1, Chapter 2). The

graphical representations of the resulting isotherm models are shown in Fig. 4.6. The

parameter values were determined using linear regression and are presented in Table

4.5. The high value of 0.98 obtained for the correlation coefficient (R2) for the Langmuir

model suggests that the model fits well with the experimental data indicating

monolayer biosorption of thorium ions onto DKSC. The maximum value obtained for Q0

was 125 mg/g for DKSC. The essential features of Langmuir isotherm model can be

elaborated using two dimensionless parameters namely, the separation factor (𝑅𝐿) and

the surface coverage (θ). Fig. 4.6 depicts the plots of RL and θ against the initial Th

0

2

4

6

8

10

12

14

16

0

20

40

60

80

100

120

140

0 200 400 600 800

Kd , L/g q e,

mg/

g

Ce,mg/L

qeKd

66

concentration (Ci, mg/L). The results reveal that the 𝑅𝐿 values obtained are between 0

and 1 (0<𝑅𝐿<1) suggesting that the biosorption of thorium using the DKSC is favourable.

Also, the θ value increased with Ci until the sites on the DKSC were saturated which also

indicates sorption with monolayer coverage {Dada et al., 2012, Nagapal et al., 2011,

Yuvaraja et al., 2014).

Table 4.5: Parameter values derived from isotherm models.

Langmuir isotherm model

𝑸𝟎 125.00 𝑹𝟐= 0.98

𝑲𝑳 0.13

Freundlich isotherm model

𝑲𝒇 1.54 𝑅2= 0.66

n 4.85

Figure 4.6: Validation of equilibrium data through a comparison of different adsorption

isotherm model, (a) Langmuir model and (b) Freundlich model. Error bars are for ±5 %

variation.

0

20

40

60

80

100

120

140

160

180

200

0 100 200 300 400 500 600 700

q e, m

g/g

Ce mg/L

(a) qe, expLangmuir

0

20

40

60

80

100

120

140

160

180

0 100 200 300 400 500 600 700 800

q e, m

g/g

Ce mg/L

(b) qe, exp

Freundlich

67

Figure 4.7: Separation factor (RL) and surface coverage (θ) as function of Ci.

4.3.4. Kinetic studies of diffusion and mass transfer modeling

The kinetic studies were carried out using initial Th(IV) concentrations of 25, 50 and

100 mg/L for 3 hours at 25⁰C in a working volume of 0.2 L to determine the mechanism

of biosorption process. The kinetic data obtained were modeled using the linear forms

of the pseudo-first-order, pseudo-second-order, and intraparticle diffusion models

(Section 2.2.2, Chapter 2). The performance of each of the models and the model

parameters used are presented in Table 4.6.

High correlation coefficient values (R2 = 0.9999 (25 mg/L), 0.9999 (50 mg/L)

and 1.0000 (100 mg/L)) were obtained for the pseudo-second order model for the

concentration ranges used indicating that the experimental data fitted well with the

model. Further, the close agreement between experimental 𝑞𝑒 values and those

predicted by the pseudo-second order model suggests that the model was suitable for

representing the kinetics of thorium uptake onto DKSC (Fig. 4.8). Also, the results

suggest that the overall rate of biosorption may be influenced by chemisorptions which

involves ion exchange on the sorption sites (Ahmed et al., 2014, Nagpal et al., 2010).

0

0.25

0.5

0.75

1

1.25

0

0.05

0.1

0.15

0.2

0.25

0 150 300 450 600 750

θ R L

Ci, mg/L

RL 8

68

Figure 4.8: Experimental data(●) and Pseudo-second order model ( ).

Table 4.6: Kinetic model parameters for thorium biosorption.

Model parameters

25 mg/L 50 mg/L 100 mg/L

Pseudo-first order

𝒒𝒆 (exp) 46.67 81.92 179.69

𝒒𝒆 2.64 2.79 2.77

𝒌𝟏 0.009 0.012 0.012

𝑹𝟐 0.78 0.80 0.88

Pseudo-second order

𝒒𝒆 (exp) 46.67 81.92 179.69

𝒒𝒆 47.62 81.97 178.57

𝒌𝟐 0.0172 0.0169 0.0241

𝑯 39.00 113.55 768.48

𝑹𝟐 0.9997 0.9999 1.0000

Intra-particle diffusion

𝒌𝒊 0.219 0.218 0.182

𝑪 43.47 78.76 176.9

𝑹𝟐 0.82 0.91 0.97

0

30

60

90

120

150

180

210

240

0 30 60 90 120 150 180 210 240

q t (m

g/g)

Contact time (min)

Exp. Data 100 mg/LExp. Data 50 mg/LExp. Data 25 mg/L

69

4.3.5. Thermodynamic studies for determining feasibility of the biosorption

process

The thermodynamic study (effect of temperature) on biosorption reveals valuable

information regarding enthalpy and entropy changes (Azouaou et al., 2010). The

biosorption of thorium studies was carried out at different temperatures to determine

thermodynamic parameters such as ∆𝐻0, ∆𝑆0, and ∆𝐺 0 using distribution coefficients

(𝐾𝑑)(Section 2.2.3, Chapter 2).

The ∆𝐻0 and ∆𝑆0 values were determined from the plot of ln Kd vs. 1/T (Fig. 4.9)

as 47.04 J/mol (slope) and 184.82 J/mol (intercept) (Table 4.7), respectively. When the

∆𝐻 values are <40 J/mol the type of adsorption can be accepted as physical process

with weak attraction of forces. Thus, in the present study the ∆𝐻 value was obtained as

47.04 J/mol representing the adsorption as chemical process involving chemical

reactions. The positive value of enthalpy change suggests that thorium biosorption

process is endothermic in nature. The positive value of entropy change designates the

increased randomness at the solid-solution interface during the adsorption of thorium

onto DKSC, also it favors complexation and stability of the biosorption process.

Figure 4.9: Temperature dependence of thorium biosorption process.

y = -5658.7x + 22.236 R² = 0.9065

0

1

2

3

4

5

6

7

0.0031 0.0032 0.0033 0.0034 0.0035

ln K

d

1/T

70

The change in Gibbs free energy (∆𝐺0) was calculated from equation 2.2 and

results are shown in Table 4.7. The ∆𝐺 values obtained for the temperatures used in this

work are negative confirming the thermodynamic feasibility and reaction spontaneity of

the thorium biosorption process with an increase in temperature. Furthermore, the

increase in ∆𝐺 values with an increase in temperature indicates that thorium

biosorption process is favoured at higher temperatures. The increase in adsorption with

temperature may be attributed to either increase in the number of active surface sites

available for adsorption onto DKSC or the desolvation of the sorbing species. Generally,

the absolute magnitude of the change in Gibbs free energy for physisorption is between

−20 and 0 kJ/mol, and chemisorption has a range from −80 to −400 kJ/mol. The results

found in this study are between −53.21 and −58.75 kJ/mol specifying the sorption to be

in between physisorption and chemisorptions, thus interpreting as physical adsorption

with an enhancement by chemical effect. Since ΔG values are between 20 and 80 kJ/mol,

adsorption type can be explained as chemisorptions with ion exchange reactions.

Seemingly the ion-exchange has a range from −20 to −80 kJ/mol, which is consistent

with the results found from isotherm and kinetic models.

Table 4.7: ∆𝐺0 values for thorium biosorption at different temperatures.

∆H° (J/mol) ∆S° (J/mol) ∆G° (kJ/ mol K)

288 K 298 K 308 K 318 K 47.0406 184.8202 -53.2089 -55.0571 -56.9053 -58.7535

4.4. Conclusions

The present work revealed that DKSC was effective in the removal of Th(IV) from

aqueous solutions. Its efficiency in removing thorium was examinedin a batch

71

biosorption experiment. A set of selected process variables were optimised via Taguchi

robust design adapting desirability function for multivariate response optimisation. The

optimum conditions obtained include the initial Th(IV) concentration at level-4 (130

mg/L), pH at level-4 (5) and DKSC loading at the level-1 (0.25 g/L). These conditions led

to a maximum value of 260 mg/g for qe and 99.97% for R%. The Langmuir isotherm

model exhibited a good correlation with the equilibrium data, and a pseudo-second

order model fitted well the kinetic data obtained within the concentration range used.

The kinetic study suggests that chemisorption occurred during biosorption. The

thermodynamic studies revealed that thorium biosorption was a spontaneous process

and it was endothermic in nature.

72

Chapter5 Zirconium biosorption and

optimisation studies via

Box-Behnken method in RSM and

desirability approach

73

Summary

The research presented in this chapter discusses the sequestration of zirconium from

aqueous solutions using the biosorption method as a separation technique. The

biosorptivebehaviour of deoiled Karanja biomass was therefore investigated for the

removal of zirconium metal ions. The effects of process variables namely, the initial metal

concentration, the pH of the feed solution and the DKSC loading were examined, and the

parameters were optimised for the maximum biosorption capacity and bio-efficiency using

the Box-Behnken method with 33design in response surface methodology (RSM).

5.1. Introduction

Zirconium is a significant engineering material due to its corrosion-resistive traits and

has achieved significant implementation in the nuclear industry because of its

transparency to neutrons for cladding uranium fuel elements and for trapping fission

fragments. The speciation of Zr from nuclear discharge is a chief concern in nuclear

waste management and related environmental pollution control. The methods

employed for the separation of zirconium from aqueous streams and the use of different

kinds of biomasses in the recovery of this metal ion were discussed in Chapter 2

(Literature Review).

5.2. Experimental procedures

Materials and methodologies employed to in this study were described in Chapter 3

(Materials and Methodology).

74

(a) Preliminary studies

As described in Chapter 4, preliminary investigations used a univariate method to

analyse the effect of variables such as contact time, mixing speed, initial pH, and DKSC

loading by conducting experiments under batch mode.

(b) Box-Behnken design (33) in RSM

A Box-Behnken experimental design containing three factors namely, initial Zr(IV)

concentration, initial pH and DKSC loading with three levels (Table 5.1) for each

variable was implemented to study the effect of these process variables on qe and R% in

the zirconium biosorption studies. The design matrix was developed using Design

Expert software version 9.0 (trial version) that generated 15 experiments including

three center points. The details of the experiments are presented in Table 5.2 along with

the experimental and predicted responses for bio-removal efficiency (𝑅%).

Table 5.1: Levels of process variables in the Box-Behnken experimental design.

Design Variable Coded values

Uncoded values

∇xi

Initial Zr concentration (mg/L)

-1 0 +1 55 65 75 10

Initial pH -1 0 +1 2 3 4 1 DKSC loading(g/L) -1 0 +1 3 5 7 2

5.3. Results and discussions


The effect of contact time on the biosorption of zirconium onto DKSC was investigated

over a period of 4 hours using 50 mg/L of initial Zr(IV) concentration with an initial pH

of 3 and DKSC loading of 0.5 g for 100 ml. The results obtained are shown in Fig. 5.1.

They show a reduction in the Zr(IV) concentration (Ci, mg/L) with increasing time

75

Table 5.2:33 Box-Behnken design matrix for zirconium biosorption studies with

experimental and predicted results for R%.

Run

order

Coded level of

Variables

Actual level of

variables Biosorption

capacity,

𝒒𝒆(mg/g)

Bio-removal

efficiency (𝑹%)

𝐴 𝐵 𝐶 𝐴 𝐵 𝐶 Observed

response

Predicted

response

1 -1 -1 0 55 2 5 8.75 79.53 74.58

2 1 -1 0 75 2 5 9.02 60.13 60.24

3 -1 1 0 55 4 5 10.14 92.22 92.10

4 1 1 0 75 4 5 14.18 94.56 99.50

5 -1 0 -1 55 3 3 16.69 91.05 93.74

6 1 0 -1 75 3 3 23.47 93.87 91.49

7 -1 0 1 55 3 7 7.23 92.07 94.44

8 1 0 1 75 3 7 9.90 92.43 89.74

9 0 -1 -1 65 2 3 8.33 38.43 40.69

10 0 1 -1 65 4 3 17.42 80.41 77.84

11 0 -1 1 65 2 7 4.30 46.34 48.92

12 0 1 1 65 4 7 6.57 70.81 68.56

13 0 0 0 65 3 5 12.18 93.72 93.59

14 0 0 0 65 3 5 12.17 93.63 93.59

15 0 0 0 65 3 5 12.14 93.41 93.59

A-Initial Zr(IV) concentration (mg/L), B- Initial pH of feed, C-DKSC loading

which indicates the capability of DKSC as a sorbent in sequestering zirconium ions from

aqueous solutions. The effect of contact time on Zr(IV) metal uptake was investigated using

1.5 g/L DKSC. Zirconium uptake was very rapid in the first 100 minutes leading to nearly

50% of zirconium removal. Equilibrium was attained at 230 minutes. These results indicate

that a contact time of approximately 4 hours is suitable for the removal of zirconium from

aqueous streams which can be considered very short and economical in commercial

prospects for DKSC. Hence, the equilibrium time was fixed as 4 hours in further biosorption

76

Figure 5.1: Preliminary studies: Effect of contact time on zirconium biosorption onto

DKSC.

experiments. The effect of shaker speed in zirconium biosorption was found to have the same

effect as discussed in section 4.3.1. The results obtained in the preliminary investigations

have led to the idea of fixing levels using the Box-Behnken method for optimisation. The

effect of pH on the hydrolysis of zirconium ion concentration in the feed was examined by

varying it from 2 to 6. The trials found that the precipitation of zirconium hydroxides in the

aqueous solution takes place beyond pH 4 and these precipitates could be observed visually.

This may be due to the presence of hydroxide complexes (cationic or anionic) and hydroxide

precipitates in the feed solution (Sayana et al., 2016).

Monomeric hydrolysed species such as 𝑀𝑂(𝑂𝐻)−1, 𝑀(𝑂𝐻)22−, 𝑀(𝑂𝐻)3−and

polymeric hydrolysed species with general form [𝑀(𝑂𝐻)𝑥4−𝑥]𝑛were the most soluble

zirconium species present in the pH range 2-4. Also, the formation of insoluble colloidal

zirconium hydroxides occurs beyond pH 4 as reported in literature(Boveiri Monji et al.,

2008). Hence, pH values up to 4 were considered in further studies in this work. The

0

10

20

30

40

50

60

70

0

10

20

30

40

50

60

70

0 50 100 150 200 250 300 350

R%

C i, mg/

L

Contact time, minutes

Ci mg/L

R%

77

biosorption capacity was found to be lower for DKSC loading below 3 g whereas it was

almost constant for DKSC loading above 7 g. Thus, the levels of DKSC loading for this

work were chosen as 3, 5 and 7 g.

5.3.2. Multivariate optimisation of Zr(IV) biosorption process using Box-Behnken

method in RSM using desirability approach

(a) Statistical analysis of Box-Behnken (33) experimental design

Experiments were conducted according to the design specified in Section 3.5.1 and the

data collected were converted into biosorption capacity (qe) and bio-removal efficiency

(R%) according to equations 3.5 and 3.6, respectively. The results calculated were

analysedfor statistical significance employing Design Expert software version 9.0 (trial

version). The predicted values of response were obtained by full quadratic model fitting

using the software mentioned above. An empirical second-order polynomial equation

(quadratic model) relationship involving response and variables shown in equation 5.1

can be used to predict the response at given levels for each factor.

𝑅% = 93.59 − 1.74𝐴 + 14.20𝐵 − 0.26𝐶 + 10.69𝐴2 − 22.67𝐵2 − 11.92𝐶2 +

5.43𝐴𝐵 − 0.6𝐵𝐶 − 4.38𝐵𝐶 (5.1)

The correlation between the predicted and observed responses is shown in a

parity plot (Fig. 5.2). As is evident from the plot, the data points fall very close to the

straight line with 45°slopeimplying a good correlation between the observed and

predicted responses thereby confirming the quality of the model. The statistical

significance and goodness-of-fit of the model were tested using the analysis of variance

(ANOVA), and the results are shown in Table 5.3.The values obtained for the coefficient

of determination 𝑅2and adjusted 𝑅2 are 0.9798 and 0.9435, respectively which are in

reasonable agreement with those found from experimental results. The R2 (coefficient

78

of determination) value obtained in the present study suggests that 97% of the total

variationfor Zr(IV) biosorption can be revealed by the model and only 3% is left to

residual variability. The predicted 𝑅2 obtained a value of 0.67 which implies that the

present model has a block effect and adequate precision obtained a value of 16.26

(Table 5.3) which can be used to navigate the design space in the present study. The

coefficient of variation (CV) for this model is the error expressed as a percentage of the

mean. Results obtained in the present study are in agreement with those previously

reported (Mourabet et al., 2012).

Figure 5.2: Predicted response versus observed response (R%).

According to the results obtained using ANOVA from the quadratic model, the

model 𝐹-value (26.96), the model constant (𝛽0), pH (𝐵), and the lack of fit (LOF) and

interaction terms (𝐴𝐵,𝐴2,𝐵2, and𝐶2) are statistically significant whereas 𝐵𝐶 is

marginally significant at a 95% probability level (Kousha et al., 2012). The LOF analysis

has proven that the quadratic model chosen for the present system is acceptable (Islam

et al., 2009). The sum of squares (SS) obtained from ANOVA was used to calculate the

79

percentage contribution (PC) for each model term as SS strengthens the significance of

the corresponding source in the undergoing process (Feza Geyikci et al., 2012).

Table 5.3: ANOVA for response surface quadratic model.

Source SS df Mean

Square 𝑭

Value p-value Model

Coefficient Coefficient

Estimate SE

PC, % Prob> F

Model 4761.81 9 529.09 26.96 0.0010

Significant 𝛽0 93.59 2.56 ----

𝑨 24.10 1 24.10 1.23 0.32 𝛽1 -1.74 1.57 0.51

𝑩 1612.53 1 1612.53 82.17 0.03

Significant 𝛽2 14.20 1.57 34.48

𝑪 0.56 1 0.56 0.028 0.87 𝛽3 -0.26 1.57 0.01 𝑨𝑩 118.11 1 118.11 6.02 0.06 𝛽12 5.43 2.21 2.52 𝑨𝑪 1.51 1 1.51 0.077 0.79 𝛽13 -0.61 2.21 0.03 𝑩𝑪 76.63 1 76.63 3.90 0.10 𝛽23 -4.38 2.21 1.64

𝑨𝟐 421.75 1 421.75 21.49 0.01

Significant 𝛽11 10.69 2.31 9.02

𝑩𝟐 1897.18 1 1897.18 96.67 0.00 𝛽22 -22.67 2.31 40.56 𝑪𝟐 524.81 1 524.81 26.74 0.00 𝛽33 -11.92 2.31 11.22 Residual 98.12 5 19.62

Lack of

Fit 98.07 3 32.69 1311.18

0.0008 Significant

Pure Error

0.050 2 0.025 Adequate Precision=16.261

Cor Total

4859.93 14 CV=5.48

𝑅2=0.9798, Pred.𝑅2=0.6771, Adj.𝑅2=0.9435

Percentage contribution of each of the model terms was calculated using

equations (3.14) to (3.16) as discussed in section 3.6.3 and is shown schematically in

Fig. 5.3. As depicted in the figure, the quadratic terms (A2, B2and C2) demonstrated the

highest level-of-significance with a total contribution of 61%, followed by the

interaction terms (AB, BC and AC) with a total contribution of 35%. First-order terms

(A, B and C) showed the lowest level of significance with a total contribution of only 4%

representing an insignificant effect in predicting Zr(IV) biosorption efficiency. Among

the three factors considered, only the initial pH of feed (B) showed the highest level of

80

significance with a contribution of 40.56% (quadratic term) and 34.48% (first-order

term) followed by DKSC loading with 11.22% (quadratic term) as compared to other

components. Similar results were reported by previous authors(Yetilmezsoy et al.,

2009, Singh et al., 2010, E. Ozdemir, 2011, Kumar Anupam, 2011).

Figure 5.3: Schematic representation of percentage contribution.

• Interaction effect of process parameters:

Fig. 5.4 represents the 3D surface plots of the combined effects from

the 𝐴𝐵,𝐴𝐶and 𝐵𝐶 parameters for Zr(IV) biosorption using DKSC. The interactions of the

parameters 𝐴𝐵 have a significant positive effect towards the response R% where as 𝐴𝐶

and 𝐵𝐶 have an insignificant effect. Fig. 5.4.(a) depicts the interaction of AB (initial Zr

concentration-initial pH) with response (𝑅%) and the response plot can be explained as

follows. At a constant DKSC loading (5g/L), for an initial Zr concentration (𝐴) at 55

mg/L and at a pH (𝐵) of 2, the R% obtained was 79.52%. The value increased to 94.56%

as the pH was increased to 4 with the same initial Zr concentration. In comparison,

when the initial Zr concentration was increased to 65mg/L and the pH decreased to 3,

81

the R% dropped to 93.6%. At an initial Zr concentration of 75 mg/L and a pH of 2, the

lowest R% value of 60.13 % was obtained.

Figure 5.4: 3D response surface plots for (a) 𝐴𝐵with R%, (b) 𝐴𝐶 with 𝑅% and (c) 𝐵𝐶

with 𝑅%.

Similarly, the interaction of 𝐴𝐶 (initial Zr concentration-DKSC loading) with

response (R%) is illustrated in Fig. 5.4.(b). For an initial Zr concentration of (𝐴) 55

mg/L and DKSC loading (𝐵) of 3 g/L, the R% value was 91.05% at a constant pH of 3.An

increase in DKSC loading from 3 to 7 g/L with the same initial Zr concentration

increased the R% value to 92.07%. A further increase in initial Zr concentration to 65

mg/L at a DKSC loading of 5 g/L led to an increase in R% to 93.58%. At 75 mg/L of

initial Zr concentration and with a DKSC loading of 3 g/L, R% attained the highest value

of 93.87%. Fig.5.4. (c) represents the interaction of parameters BC (initial pH-DKSC

loading) with response (R%). For a constant initial Zr concentration of 65 mg/L at a pH

of 2 and a DKSC loading of 3 g/L, R% attained a value of 38.43%. An increase in pH to a

value of 3 and a DKSC loading to a value of 5g/L led R% to a maximum value of 93.72%.

82

From the above observations, it was established that among all the interactions

studied (𝐴𝐵,𝐴𝐶and 𝐵𝐶), pH and DKSC loading had the most effect on the bio-removal

efficiency. It can be inferred that an increase in pH value causes an increase in the R%

with a maximum obtained at around a pH of 3-4. The underlying phenomenon can be

explained by studying the functional groups involved in the metal binding mechanism.

FTIR spectroscopic analysis revealed that methyl, amide, carboxylic and nitro are the

potential functional groups that can actively participate in the biosorption of Zr(IV)

metal ions onto DKSC depending on the initial pH of the feed solution. At a pH of 3-4, the

maximum number of interactions occurs between cations such as Zr+4 and the possible

functional groups present on DKSC due to electrostatic attraction. At a pH of 2-3, the R%

decreases due to the electric repulsion among the Zr+4 and molecular groups along the

binding sites as H+ and H3O+ions increase (Bhatti and Amin, 2013, P. Senthil Kumar,

2011, H. Kalantari, 2014). The influence of the initial pH towards Zr(IV) biosorption can

be explained by considering the𝑝𝐻𝑝𝑧𝑐 of the DKSC. At a pH <𝑝𝐻𝑝𝑧𝑐, the surface charge of

DKSC is positive leading to the repulsion of Zr(IV) which results in low Zr(IV) sorption

onto DKSC and therefore a low R%. While at a pH>𝑝𝐻𝑝𝑧𝑐, the surface charge of DKSCis

negative and therefore Zr(IV) gets sorbed onto the DKSC with greater affinity and

consequently R% increases. Since the 𝑝𝐻𝑝𝑧𝑐of DKSC was 6.72, Zr(IV) sorption will be

maximised at pH<𝑝𝐻𝑝𝑧𝑐. The highest Zr(IV) biosorption took place in the pH range of 3-

4 because at pH >4 zirconium hydroxides are formed and are precipitated in the

aqueous solution. An increase in DKSC loading increased the extent of surface area for

sorption making more adsorption sites available for exchange; as a result, the activity of

functional groups also increased thereby leading to a higher R% (Serencam et al., 2013,

Ozdes et al., 2010, Serencam et al., 2014, Reddy et al., 2010).

83

(b) Multi-response optimisation via a desirability approach

The desirability approach is an established tool for the optimisation of design variables

including single and multiple responses. The standard desirability functions are

discussed explicitly in section 3.6.4. Design Expert software was used to find the

optimum combination of process variables (𝐴,𝐵 𝑎𝑛𝑑 𝐶) that maximised the responses

(𝑅%and 𝑞𝑒) using the desirability function simultaneously. A maximum level of initial Zr

concentration (𝐴), initial pH (𝐵) within range, a minimum level of DKSC loading (𝐶) and

a maximum level of responses (R% and𝑞𝑒) were set for maximum desirability as shown

in Table 5.4.

Table 5.4: Optimisation of individual responses (𝑑𝑖) to obtain overall desirability

response (𝐷).

Factor Goal Lower limit Upper limit Lower weight

Upper weight

Importance

Initial Zr Concentration (𝑨) Maximize 55 75 1 1 5

Initial pH (𝑩) Within range 2 4 1 1 1 DKSC loading (𝑪) Minimize 3 7 1 1 3

𝑹% Maximize 38.43 93.72 1 1 5 𝒒𝒆(mg/g) Maximize 4.30 23.46 1 1 5

The results show that the initial pH is the major factor that needs to be considered

in the biosorption studies of Zr using DKSC, followed by the DKSC loading and the initial

Zr concentration. From 16 starting points in the response surface changes, the best local

maximum was found to be at an initial Zr concentration (𝐴)of 74.99 mg/L, an initial pH

(𝐵) of 3.58, and a DKSC loading (𝐶) of 3.00g/L, which produced a maximum bio-removal

efficiency(R%) of 97.80% and biosorption capacity (𝑞𝑒) of 23.44 mg/g with overall

desirability (𝐷) of 0.99 as shown in Fig. 5.5. To validate the optimised parameters,

84

confirmatory runs were conducted using process parameters. The experimental results

obtained were found to be close to optimised values (Table 5.5).

Figure 5.5: Desirability ramp for numerical optimisation of five goals considered

Table 5.5: Optimised and confirmative values of the process parameters for maximum

responses (𝑅% and 𝑞𝑒)

Process parameters

Optimized Values

(predicted)

Confirmation Values

(experimental)

𝑹% 97.80 95.67

𝒒𝒆, mg/g 23.44 22.76

Initial Zr concentration (𝑨), mg/L 74.99 75

Initial pH (𝑩) 3.57 3.6

DKSC loading (𝑪), g/L 3.00 3

5.3.3. Equilibrium studies and adsorption isotherm modeling

Equilibrium studies were carried out using 0.3g of DKSC in 0.1L of working solution.

The solutions were mixed at 25⁰C in a shaker running at 200 rpm for 4 hours. The initial

85

Zr concentration was varied from 20 to 100mg/L. Fig. 5.6 shows a plot of biosorption

capacity (qe) and distribution capacity (Kd) as a function of equilibrium Zr concentration

(Ce). The parameter𝑞𝑒increased considerably with an increase in 𝐶𝑒due to the increase

in the initial Zr concentration (𝐶𝑖). As 𝐶𝑖increases, the interaction between the sorbate

and sorbent also increases (Reddy et al., 2010, Duygu Ozdes et al., 2010, Senthil Kumar

et al., 2011). The𝐾𝑑 value decreased from 2.958 L/g to 1.582 L/g for an increase in Ce

ranging from 2 to 16 mg/L at a DKSC loading of 3 g/L. Higher values of Kd were obtained

for lower Ce implying an important feature that DKSC has the ability to treat large

volumes of low concentration metal wastes. The 𝐾𝑑 values achieved in this research

were very high when compared to other industrial adsorbents, which have 𝐾𝑑values as

low as 0.010 L/g (Akhtar et al., 2008).

Figure 5.6: qe and Kd as a function of Ce.

The adsorption isotherms were obtained at a fixed temperature and fixed DKSC

loading using isotherm model equations in linearised forms as discussed in Section 2.2.1

(Chapter 2). The graphical representation of model isotherms is shown in Fig. 5.7, and

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

0

5

10

15

20

25

30

35

40

0 3 6 9 12 15 18

Kd (L/g) q e

(mg/

g)

Ce (mg/L)

qe

Kd

86

the calculated model parameters are tabulated in Table 5.6 along with correlation

coefficients.

Table 5.6: Isotherm model parameters obtained in the biosorption of Zr(IV) onto DKSC.

Langmuir Q0 KL 𝑹𝟐

Freundlich 𝑲𝒂𝒅 𝒏 𝑹𝟐

38.46 0.08 0.98 3.15 1.35 0.99

Temkin 𝑩 AT 𝑹𝟐

D-R 𝒒𝒔 𝑲𝒂𝒅 𝑹𝟐

0.10 60.81 0.94 17.90 0.14 0.79

Figure 5.7: Adsorption isotherms at optimised conditions (initial pH: 3.6, DKSC loading:

3 g/L and initial Zr concentration: 18 to 90 mg/L).

High correlation coefficients were obtained for linear plots corresponding to the

Freundlich and Langmuir isotherm models indicating that the equilibrium data agreed

well with both models thus indicating the heterogeneous multilayer biosorption of

Zr(IV) onto DKSC. The parameter values obtained signified a greater extent of

biosorption (better 𝐾𝑎𝑑) with greater heterogeneity (with 1/𝑛 = 0.74) and favourable

biosorption of Zr(IV) onto DKSC (𝑛=1.35) i.e., indicating the heterogeneous multilayer

0

5

10

15

20

25

30

0 3 6 9 12 15 18

q e, m

g/g

Ce, mg/L

Exp. dataLangmuirFreundlichDR isothermTemkin

87

adsorption of Zr ions onto DKSC surface (Varala et al., 2016). The qe,model values were

estimated from the model equations using regression analysis and are shown in Fig. 5.7

along with experimental data. As can be seen from Fig. 5.7, the Freundlich model fitted

the experimental data better compared to other models.

The results suggested that the biosorption of Zr(IV) is proportional to the square

of the number of vacant sites on DKSC and the overall rate of biosorption may be

affected by chemisorption, i.e., chemical reactions involving an exchange of electrons

between DKSC and Zr(IV). Also, the calculated biosorption capacities were comparable

to the experimental data within the concentration range used. Most of the previous

studies on biosorption reported that the metal biosorption process is a pseudo-second

order process and the results of our study confirmed the same (Sharma et al., 2009,

Ercan Özdemir, 2011, Ruhan Altun Anayurt, 2009)

5.3.3. Kinetic studies with diffusion and mass transfer modeling

Kinetic studies help to study the effect of contact time and are essential to

describe the biosorption rate. The kinetic models assist in determining the rate

controlling step for the biosorption process. Kinetic studies in this work were carried

out using different initial Zr concentrations of 60 and 80 mg/L in a working volume of

0.2 L using 1.5 g/L DKSC for a constant contact time of 4 hours at 25⁰C and using a

shaker running at200 rpm.To determine the mechanism of the biosorption process,

kinetic models specified in section 2.2.2 (Chapter 2)were applied to the experimental

data. The parameters for all kinetic models were calculated from the linear plots and are

tabulated in Table 5.7 along with their correlation coefficients. On close examination of

correlation coefficients, it can be found that the pseudo-second order model leads to the

88

best correlation (𝑅2= 0.99 for 60mg/L and 0.994 for 80 mg/L) for the kinetic data

compared to other models.

Table 5.7: Kinetic model parameter values for Zr(IV) sorption onto DKSC.

60 mg/L 80 mg/L 60 mg/L 80 mg/L

Pseudo-first order Intraparticle diffusion

𝒒𝒆 (exp) 3.04 7.87 𝑞𝑒 (exp) 3.04 7.87

𝒒𝒆 1.36 4.59 𝑘𝑖 0.067 0.23 𝒌𝟏 0.01 0.01 𝐶 1.8 4.19 𝑹𝟐 0.81 0.88 𝑅2 0.94 0.81 Pseudo-second order Elovich

𝒒𝒆 (exp) 3.04 7.87 𝑞𝑒 (exp) 3.04 7.87 𝒒𝒆 2.89 8.06 𝐴 21.09 5.21 𝒌𝟐 0.03 0.09 𝛢 3.49 0.90 𝑯 7.77E-4 7.95E-5 𝛣 3.49 0.90 𝑹𝟐 0.99 0.99 𝑅2 0.85 0.89

5.3. Conclusions

The present research focused on the utilisation of an agro-industrial waste biomass

namely DKSC as a sorbent for the biosorption of Zr(IV) from aqueous solutions.

Multivariate optimisation was carried out for the process variables namely initial Zr

concentration, initial pH, and DKSC loading, using Box-Behnken design in RSM along

with desirability approach for maximum bio-removal efficiency (𝑅%) and biosorption

capacity (𝑞𝑒).Regression analysis showed that a quadratic model provided the best fit to

the experimental data with a coefficient of determination (𝑅2) value of 0.98 and F-Value

of 26.96. The desirability function recorded a maximum R% of 97.8% and a 𝑞𝑒of 23.44

mg/g for an initial metal concentration of 74.99 mg/L at a pH of 3.57 and for3.00 g/L of

DKSC loading. At a desirability value of 0.99, these conditions were confirmed as the

optimised process conditions. The equilibrium data agreed well with the Freundlich

89

isotherm model and the kinetic data agreed well with a pseudo-second-order equation

with a correlation coefficient of 0.99 for the concentration range considered. Data for

the distribution coefficient (𝐾𝑑) obtained in the present study was 2.96 L/g which is

much higher than those of other industrial adsorbents.

90

Chapter 6 Desorption studies for the

isolation of radionuclides

from loaded biomass

91

Summary

This chapter focuses on the efficient individual recovery of the radionuclides (Th and Zr)

from the loaded DKSC using different eluting media. The desorption step is useful in the

isolation of sorbed metal ions as well as in the regeneration of loaded biomass that can be

reused in further cycles. The primary factors affecting the desorption were optimised

employing the Taguchi 𝐿18 (2132) mixed level design for the maximum desorption

efficiency(𝐷%) and it was found that the eluent concentration was the major factor in

desorption. The optimised conditions were found to be as follows: 1M HCl at an L/S ratio of

7 with a recovery of 96%, and 0.1M NaHCO3 at an L/S ratio 3 with a recovery of 69% for

thorium and zirconium, respectively. It was also proved that the desorption step

regenerated the biosorbent which possesses properties similar to that of native DKSC. The

desorption kinetics for both thorium and zirconium followed the pseudo-second order rate

equation at optimal conditions.

6.1. Introduction

Desorption of metal ions from loaded biosorbents is accomplished using an elution

process that involves the use of an appropriate eluting/desorbing medium (Elwakeel et

al., 2014, Elwakeel et al., 2017).The mechanisms of desorption and biosorption are

similar with both involving ion exchange. The detailed mechanisms involved in the

desorption process were discussed explicitly in Chapter 2(Literature review).

6.2. Experimental investigations

The detailed experimental procedures used for the desorption studies were described

in section 3.5.2 (Chapter 3).

92

(a) Preliminary studies:

Screening experiments were carried out using eight eluents containing competing

counter ions (sodium cations), proton exchangers (mineral acids) and complexing

agents (chlorine, carbonate and bicarbonate anions). These were used to treat the

metal-laden biomass to recover the sorbed metal ions, to determine an effective

desorption technique based on the desorption efficiency (𝐷%), and to ascertain the

elution potential of each eluent towards the desorption of radionuclides (Th and Zr)

from loaded biomass. The L/S ratio and the eluent concentration used in the

experiments are shown in Table 6.1.

Table 6.1: Range of parameters considered for the desorption studies

Parameters Range

Eluent type HNO3, HCl, H2SO4, NaOH, NaCl, CH3COONa, Na2CO3, NaHCO3

L/S ratio 1

Eluent concentration 0.1M

(b) Taguchi L18 (2132) OA experimental design for metal elution:

A Taguchi orthogonal array 𝐿18 (2132) design consisting of 3 factors having mixed levels

(one factor at two levels and two factors at three levels) was employed to examine the

influence of process variables such as eluent type, eluent concentration, and L/S ratio

on the desorption efficiency (𝐷%) for the desorption of thorium and zirconium from

loaded biomass. Three typical eluent concentrations of 0.01, 0.1 and 1M were tested

along with an L/S ratio in the range 3-10. The factors and their levels were selected

based on the preliminary assessment and are shown in Table 6.2. MINITAB17 statistical

software (free trial) was used for the generation of the experimental design matrix

involving the chosen factors at their respective levels as shown in Table 6.3. 𝐷% was

93

chosen as the response variable in the present study, and the target was to achieve a

higher 𝐷%; hence, the larger-is-better criterion was selected for the S/N ratio (Equation

3.9).

Table 6.2: Factors and levels considered for the Taguchi mixed design 𝐿18 (2132)

model.

Factors/Levels 1 2 3 1 2 3

Thorium desorption Zirconium desorption

Eluent 𝐴 HCl HNO3 - H2SO4 NaHCO3 -

L/S ratio 𝐵 3 7 10 3 7 10

Eluent Concentration (M) 𝐶 0.01 0.1 1 0.01 0.1 1

Table 6.3: Taguchi L18 orthogonal array design for the desorption process

Experiment run order

Eluant

L/S ratio

Eluant concentration

Thorium desorption Zirconium desorption D% S/N

ratio D% S/N

ratio 1 1 1 1 7.42 17.41 40.54 32.16 2 1 1 2 42.81 32.63 54.90 34.79 3 1 1 3 92.18 39.29 5.63 15.01 4 1 2 1 24.61 27.82 0.18 14.90 5 1 2 2 82.59 38.34 58.13 35.29 6 1 2 3 96.00 39.64 8.43 18.52 7 1 3 1 17.94 25.08 0.56 5.04 8 1 3 2 14.18 23.03 62.60 35.93 9 1 3 3 66.61 36.47 1.03 0.26

10 2 1 1 13.39 22.53 5.89 15.40 11 2 1 2 69.74 36.87 69.15 36.79 12 2 1 3 90.54 39.14 69.82 36.88 13 2 2 1 28.06 28.97 12.25 21.76 14 2 2 2 63.44 36.05 76.10 37.63 15 2 2 3 83.51 38.43 59.05 35.42 16 2 3 1 1.89 5.53 18.85 25.51 17 2 3 2 56.10 34.98 62.60 35.93 18 2 3 3 73.47 37.32 69.08 36.79

94

6.3. Results and discussion


The results obtained in the preliminary evaluation are shown on a Pareto chart (Fig.

6.1). As shown in the figure, the results indicated that both HNO3 and HCl achieved

satisfactory values for D% in the metal elution process for thorium. This was due to

protonation of carboxyl, carbonyl or hydroxyl groups of the biomass all of which do not

attract the positively charged thorium (Th+4) ions.Therefore, the protons replace the

bound thorium ions and release the thorium ions into the recovery solution (Wankasi et

al., 2005). Similarly, NaHCO3 and HNO3 led to the highest values of 𝐷% in the desorption

of zirconium.

Figure 6.1: Preliminary studies for desorption of thorium (Th-D%) and

zirconium (Zr-D%) (0.1M concentration, L/S ratio: 1, 200 rpm and 25°C)

The recovery of Th and Zr in the elution step decreased depending on the recovery

media according to the following order;

Thorium desorption : HNO3 > HCl >H2SO4 > NaHCO3 >NaOH>NaCl> CH3COONa > Na2CO3

0102030405060708090

100

%D

Eluent type

Th-D%

Zr-D%

95

Zirconium desorption: NaHCO3 > H2SO4 > CH3COONa > Na2CO3 >NaOH> HCl > HNO3

>NaCl.

Based on the results, the eluents HNO3 and HCl for thorium, and NaHCO3 and HNO3

for zirconium were chosen for further optimisation studies using the Taguchi design.

During desorption experiments, the colour of the biomass was noted to change both in

the acidic and basic media in proportion to their strength. Also, the eluate was found to

change in colour after desorption as the soluble proteins are eluted from the biomass

(ALDOR et al., 1995, Jnr, 2006)).

6.3.2. Statistical significance and optimisation of thorium and zirconium

desorption using the Taguchi L18 mixed level array design

The experiments were conducted using the Taguchi 𝐿18 mixed level design matrix

(Table 6.3). 𝐷%was chosen as the response in the optimisation method. The results

obtained from the experimental runs were transformed into an S/N ratio (larger-is-

better criterion) as the aim was to maximise D%. The Minitab17 statistical software was

used for the interpretation of results which are shown in Table 6.3.

(a) Thorium elution from loaded biomass (Th-DKSC)

Response tables for the calculated S/N ratios are shown in Table 6.4. It can be

interpreted from the data shown in the table that eluent concentration was the

predominant factor that influenced the thorium desorption significantly, followed by

L/S ratio and eluent type.

96

Table 6.4: Response table for S/N ratio (larger-is-better) in the thorium desorption

studies

Factors/Levels Eluent L/S ratio Eluant

concentration

1 31.08 31.31 21.22

2 31.09 34.87 33.65

3 --- 27.07 38.38

Delta 0.01 7.81 17.16

Rank 3 2 1

Figure 6.2: Percentage Contribution of factors for thorium desorption

Fig. 6.2 depicts a pie chart showing the percentage contribution (PC) that was

calculated using the sum of squares (𝑆𝑆)calculated using equations 3.14 to 3.16 (Section

3.6.3). The chart emphasises the significance of the corresponding factor in the process

under consideration (Sayanasree et al., 2016). The pie graph further illustrates that the

eluent concentration is the leading factor with the highest level-of-significance,

contributing 88% for the recovery of thorium. In comparison, the L/S ratio contributed

only 12% and eluent type provided the least (nearly 0%) in predicting the desorption

efficiency.

0% 12%

88%

EA

L/S ratio

EAConcentration

97

Figure 6.3: Main effect plots of factors by S/N ratios (larger-is-better) for thorium

desorption.

Fig. 6.3 shows the main effect plots for three factors (eluent, L/S ratio, and eluent

concentration) and S/N ratios (larger-is-better) calculated (obtained from Table 6.3) for

thorium in the desorption studies. For the recovery of thorium, both eluents (Eluting

agents) HCl and HNO3 were judged to be suitable due to the fact that thorium metal ions

can be desorbed using acidic solutions (Gok and Aytas, 2013, Bhalara et al., 2014,

Sayanasree Varala and Satyavathi, 2016, VOLESKY, 1981)Generally, 𝐷% increased with

increasing L/S ratio and 𝑞𝑑𝑒𝑠also increased due to the high metal concentrations

released into the eluent. A very large increase in L/S ratio results in a decrease in 𝐷%

due to an increased accumulation of metal ions over the biomass leading to a new

equilibrium. It is preferable to use low L/S ratios because high metal concentration can

be achieved using a small volume of eluent (Vı´tor, 2007). An increase in eluent

concentration increases 𝐷% due to the accumulation of 𝐻+ ions in the eluant solution

that increase the concentration gradient between metal ions and protons and result in

an enhancement of the driving force for ion-exchange, thus replacing the metal ions

over the biomass surface (Zhang and Wang, 2015, Jnr, 2006). The optimised conditions

HNO3HCl

40

35

30

25

201073 1.000.100.01

EA

Mea

n of

SN

ratio

s

L/S ratio EA Concen

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Larger is better

98

can be assessed as 𝐴1𝐵2𝐶3 and 𝐴2𝐵2𝐶3i.e., an eluent HCl/HNO3 of 1M concentration at a

L/S ratio of 7 that led to 𝐷% values of 96% and 83% (Experiment run order 6 and 15 in

Table 6.3) from the Taguchi OA design for thorium. As the 𝐴1𝐵2𝐶3 combination leads to

a maximum 𝐷%, these conditions were chosen as the best optimum process

parameters for the effective recovery of thorium metal ions from loaded DKSC (Th-

DKSC).

(b) Zirconium elution from loaded biomass (Zr-DKSC)

Table 6.4 shows the response tables obtained for the calculated S/N ratios (larger-is-

better). It was found that eluent concentration was the major factor that influenced the

response most thus ranking first in the zirconium desorption. This was followed by

eluent type and L/S ratio of ranks 2 and 3, respectively.

Table 6.5: Response table for S/N ratios (larger-is-better) in the zirconium desorption

studies

Factors/Levels Eluent type L/S ratio Eluent

concentration

1 16.89 28.51 12.48

2 31.35 22.29 36.06

3 --- 21.56 23.81

Delta 14.46 6.94 23.58

Rank 2 3 1

The pie graph (Fig. 6.4) illustrates that eluent concentration is the main factor with the

highest level-of-significance contributing 75% for the recovery of zirconium from the

loaded biomass. The L/S ratio and eluent type are the second and third major factors

contributing 24% and 1% towards predicting the desorption efficiency of zirconium.

99

Figure 6.4: Percentage Contribution of factors for zirconium desorption.

Figure 6.5:Main effect plots of factors by S/N ratios (larger-is-better) in zirconium

desorption

The performance of H2SO4 and NaHCO3 as eluting agents for zirconium

desorption can be explainedby considering the concentration of the eluting agents. Fig.

6.5 displays the main effect plots of factors by S/N ratios (larger-is-better) in zirconium

desorption. An increase in eluent concentration leads to an increase in 𝐷% up to a

certain limit and then decreases because a higher number of protons increases the

electrostatic repulsion among metal ions thus inhibiting the desorption process. Thus,

24%

1%

75%

EA

L/S ratio

EA Concentration

NaHCO3H2SO4

35

30

25

20

15

101073 1.000.100.01

EA

Mea

n of

SN

ratio

s

L/S Ratio EA Concen

Main Effects Plot for SN ratiosData Means

Signal-to-noise: Larger is better

100

the optimised conditions obtained are𝐴2𝐵1𝐶2 i.e., 0.1M of the NaHCO3 solution at a L/S

ratio of 3 that leads to 69% for 𝐷% (Experiment run order 11 in Table 6.3) from the

Taguchi OA design for zirconium recovery. In the first cycle of the desorption, 69% of

the Zr has been recovered from the loaded biomass. The sorbent can only be recycled

after the complete recovery of Zr from the loaded biomass, which means a few more

desorption cycles have to be carried out before the biomass is reused.

The results attained in this work agree with the results of many previous studies

reported in the literature (Gok and Aytas, 2013, Anirudhan et al., 2010, Akhtar et al.,

2008, Bhalara et al., 2014, A. Hanif, 2013). The optimal combinations recommended by

the Taguchi mixed level design for the desorption of radionuclides (Th and Zr) were

already present in the experimental design; hence no further experimental runs were

required for confirmation.

6.3.3. Desorption kinetics evaluation

The experimental kinetic data was validated using linearised pseudo-first order and

second order desorption kinetic models (Table 2.3, Chapter 2). The model parameters

computed are shown in Table 6.6 along with regression coefficients (𝑅2).

Table 6.6:Kinetic model parameters obtained for thorium and desorption under

optimised experimental conditions.

Radionuclide 𝒒𝒆,

exp

Pseudo first order Pseudo second order

𝐤𝟏𝐝 𝐪𝐞,model 𝐑𝟐 𝐤𝟐𝐝 𝐪𝐞, model 𝐑𝟐

Thorium 13.19 0.01 98.75 0.85 0.01 13.33 0.99

Zirconium 1.52 0.01 8.06 0.98 0.03 1.49 0.98

101

Figure 6.5: Desorption kinetics at optimum process conditions.

Among the two models verified for the desorption kinetics, the pseudo-second

order model was shown to provide a better association with the experimental data with

𝑎𝑛 𝑅2≥ 0.98 (higher than the 𝑅2 values obtained for the pseudo-first order model) thus

indicating that the desorption kinetics for both thorium and zirconium follow pseudo-

second order kinetics. Although pseudo-first order kinetic model also exhibit a good fit

to experimental data with 𝑅2> 0.97 for zirconium desorption, the equilibrium value

calculated from the model (𝑞𝑒,𝑚, model) was significantly higher than the one calculated

from the experimental data (𝑞𝑒 , exp). The close agreement between the equilibrium

capacity (𝑞𝑒,𝑚, model) values predicted using the pseudo-second order model equation

and the experimental values (𝑞𝑒 , experimental) show the capability of the model

equation in predicting the desorption kinetics for both Th and Zr. This observation was

further confirmed by the close agreement between the experimental values of 𝑞𝑒and

those predicted by the pseudo-second order model (dashed line) shown in Fig. 6.5

which suggests that the desorption mechanism is due to an ion exchange reaction

between the eluant and sorbed metal ions onto the biosorbent across the active sites.

0

1

2

3

4

5

6

7

0

20

40

60

80

100

120

0 50 100 150 200 250 300 350 400

qt,d (m

g/g) q t,d (m

g/g)

Time (minutes)

Th - second order modelZr - second order modelThorium Exp. kineticszirconium Exp. kinetics

102

The observations in the present research agree with findings reported in many previous

studies in the literature (Njikam and Schiewer, 2012, Jyi-Yeong Tseng and Dar-Ren Ji,

2009, Akin sahbaz, 2015).

6.4. Conclusions

The reversibility of the biosorption process through a desorption method for the

recovery of the radionuclides Th and Zr from loaded biosorbentwas investigated to

establish a regeneration step for biomass. The process variables that affect the recovery

were optimised using the Taguchi mixed level design 𝐿18(2132) for the maximum

desorption efficiency. From the research carried out, it can be concluded that 1M HCl

and 0.1M NaHCO3solutions can be used for the efficient recovery of Th and Zr from

loaded DKSCwith a maximum 𝐷% of 96% and 69% at a L/S ratio of 7 and 3,

respectively. The desorption trend followed pseudo-second order kinetics with

calculated 𝑅2> 0.98 for both Th and Zr.

103

Chapter 7 Characterisation of deoiled

Karanja biomass, a novel

biosorbent for radionuclides

104

Summary

The present chapter describes the characterisation of a biosorbent, namely deoiled

Karanja biomass. The information about the biomass is not available in the literature.

Hence, the characteristics of deoiled Karanja biomass as a sorbent and its behavior in the

biosorption and desorption processes wereexplored. The DKSC was characterised using

SEM, FTIR and standard NREL methods to determine the possible mechanism involved in

the biosorption and desorption studies involving Th(IV) and Zr(IV). SEM analysis

demonstrated that a physical pre-treatment method followed increased the porosity and

therefore the surface area of the biosorbent. FTIR spectroscopic analysis confirmed the

participation of methyl, carboxyl, amine, alkane and nitro functional groups in the

biosorption process and the same functional groups were re-established in the desorption

process thus validating the regeneration of biomass.

7.1. Introduction

The application of deoiled Karanja biomass as a novel sorbent for the separation of

radionuclides from aqueous streams was discussed in previous chapters. This study

established the agro-industrial waste as a sorbent for the isolation of Zr and Th

radionuclides from aqueous streams. This process can be modified further and adapted

as a pollution control technology in nuclear industries dealing with radionuclide waste

materials. However, to know the performance of deoiled Karanja biomass as a sorbent

in the biosorption of thorium and zirconium, the characteristics of the sorbent need to

be identified, and thus the characterisation of deoiled Karanja biomass is necessary. The

general characterisation techniques used for the various adsorbents were discussed in

Chapter 1 (Introduction).

105

7.2. Materials and Methods

The materials and methods employed to carry out the present research were described

in Chapter 2 (Materials and Methodology).

7.3. Results and Discussions

The DKSC obtained from the local market was pre-treated before using it as a sorbent in

the biosorption process as discussed in Section 3.2.1. The metal loaded DKSC and

regenerated DKSC was washed with demineralised water on filter paper, dried and

subjected to various characterisation techniques. SEM and FTIR analyses,

physicochemical properties determination, and elemental analysis was carried out for

the native DKSC, the metal loaded DKSC and the regenerated DKSC. The biomass

samples are hereafter denoted as DKSC, Th-DKSC, Zr-DKSC, R_DKSCTh andR_DKSCZr for

the pre-treated biomass (sorbent), the thorium-loaded biomass, the zirconium-loaded

biomass, and the regenerated biomasses obtained from the thorium and zirconium

desorption studies, respectively.

7.3.1. Physico-chemical properties determination through standard NREL methods

Deoiled Karanja biomass was characterised by determining its physicochemical

properties using standard National Renewable Energy Laboratory – Laboratory

Analytical Procedures (NREL-LAP) and ultimate (CHNS) analyses. The results of the

analyses are shown in Table 7.1 for the pre-treated, the metal-loaded and the

regenerated DKSC.

106

Table 7.1: Physico-chemical properties of DKSC at various stages of biosorption and

desorption processes.

Properties DKSC Th-DKSC Zr-DKSC R_DKSCTh R_DKSCZr

pH 4.92 3.36 3.55 4.83 4.54

Moisture Content 5.60 8.10 7.32 4.90 4.76

Bulk density (g/cm3) 0.451 0.22 0.31 0.43 0.41

Surface area (m2/g) 119 --- 58.2 121 125

𝒑𝑯𝒑𝒛𝒄 6.72 --- --- 6.03 6.45

Ultimate (CHNS) Analysis

C% 50.90 49.08 51.00 49.26 50.23

H% 6.65 7.06 7.96 6.54 6.12

N% 4.44 3.43 3.63 4.27 4.01

S% 0.17 0.14 -- 0.13 0.19

R_DKSCTh-Regenerated biomass from thorium desorption

R_DKSCZr-Regenerated biomass from zirconium desorption

Figure 7.1: 𝛥𝑝𝐻 versus 𝑝𝐻𝑖for the determination of 𝑝𝐻𝑝𝑧𝑐of DKSC.

The pHpzc or point zero charge of the sorbent depends on the pH of the metal

solution in which the sorbent is suspendedforadsorption. It is an essential characteristic

that aids in the determination of the pH at which the sorbent surface has net electrical

neutrality. From Table 7.1, it can be concluded that regenerated biomass (R-DKSCTh and

107

R-DKSCZr) is analogous to native DKSC in its physicochemicalcharacterisation. In

addition to these, the values obtained for the pHpzc of the R-DKSC were consistent with

those ofthe native DKSC which indicates that the elution process could regenerate DKSC

with properties that are similar to those of pure DKSC.

7.3.2. Fourier Transform Infrared Spectroscopic (FTIR) analysis

FTIR analysis was used to understand and identify the functional groups that

participate in the biosorption process.

Native (pure) DKSC; Data shown in Fig.7.2(a) and Fig. 7.3(a) correspond to FTIR

spectra of native DKSC before biosorption. The IR spectrum containsfive major peaks at

2925.17, 2854.28, 1709.51, 1628.36, 1106.01, 1027.0cm-1, and also several broad peaks

at 3567.69, 3448.96, 1516.02, 1406.18, 1241.81 and 723.93 cm-1. The broad peaks

observed at 3567.69cm-1 and 3448.96cm-1 indicate –OH (alcoholic) and/or N-H(amino)

symmetrical stretching groups respectively, while the strong peaks near 2925.17cm-1

and 2854.28cm-1 correspond to the CH2symmetrical stretching vibration of the methyl

functional group. Other sharp peaks at 1709.51cm-1 and 1628.36cm-1 may be due to C=O

stretching vibrations of the amide group and indicates the presence of either carboxylic

(-COO ) or quinone (-C=O ) groups. A peak at 1516.02cm-1 is due to the asymmetric

stretching of –NO nitro group, whereas the one at 1460.18cm-1 corresponds to the C=O

stretching vibration of the amide group. A broad peak observed at 1241.81cm-1 is due to

a –C-O stretching vibration and/or a –C-O-H asymmetric stretching vibration of the

COOH group, and the strong peak observed at 1027.0cm-1 indicates a –C-N stretching

vibration, while the band at 760.39 cm-1 represents the C-H bend of aromatics. These

observations confirm the presence of various ionizable functional groups such as

hydroxyl, amine, methyl, carbonyl, and carboxyl on DKSC (Deschatre et al., 2015). The

108

IR spectrum of the metal loaded DKSC (Th-DKSC and Zr-DKSC) is displayed in Fig.7.2(b)

and Fig.7.3 (b). It can be seen that they are different from the IR spectrum of pure DKSC.

The variations in the IR spectra of the metal loaded DKSC compared to pure DKSC,

especially the modifications in the absorption bands in lower or higher wave numbers,

indicates the interaction of the functional groups with the radionuclides. An absorption

band transfer to lower frequencies designates a weak bond while a shift to higher

frequencies indicates a stronger bond (Yuvaraja et al., 2014, G. Yuvaraja, 2014).

Thorium-loaded DKSC (Th-DKSC) (Fig. 7.2(b)): Compared to the spectrum of the

original sample (before biosorption), an appearance of a new broad envelope near

3403.89 cm-1 in the spectrum of the loaded DKSC is due to the physical adsorption of

water molecules and also due to the binding of thorium with hydroxyl and amino

groups, i.e., proteins(Yusan et al., 2012, Yuan-You Feng Su Tang Jun Liu Ning,

2015)).Enhancement with a shift in the band at 2924.61 cm-1 (originally at 2925.17 cm-

1) and an appearance of a prominent band at 2851.54 cm-1are both an indication of the

increase in aldehydicC-H and alkane C-H stretching vibrations due to the formation of

the metal complex after biosorption. A new sharp band at 1517.20 cm-1 and the

reduction and shift of the band at 1637.65 cm-1 (originally at 1628.36 cm-1) represents

the possible bonding of amine -NH or amides (–C=O) with Th(IV). The appearance of a

sharper peak which has shifted from1460.18 to 1459.07 cm-1can be attributed to

increasing interaction of Th(IV) with carboxylic (-COOH) groups during metal

complexation reactions. A change of the band at 1025.02 cm-1 (originally at 1027.00 cm-

1) and a dampening of the band at 1155.10 cm-1 are due to a weak electrostatic

interaction of Th(IV) with DKSC. Two new weak bands at 760.09 cm-1 may be

attributable to C-H bending bonds of aromatics after biosorption, and the band at

109

719.94 cm-1can be assigned to weakly bonding of oxygen atom with thorium as Th-O

and O-Th-O bonds (Boveiri Monji et al., 2014, Anirudhan et al., 2010). These shifts may

be acknowledged as the biosorption of Th(IV) ions onto DKSC associated with

amine/amides, carboxyl, and alkyl functional groups through strong complexation

reactions and weak electrostatic forces.

Figure 7.2: FTIR spectrum of (a) Pure DKSC, (b) thorium-loaded DKSC (Th-DKSC) and

(c) Regenerated DKSC (R-DKSCTh).

110

Figure 7.3:FTIR Spectra of (a) Pure DKSC, (b) zirconium-loaded DKSC (Zr-DKSC) and

(c) Regenerated DKSC (R-DKSCZr).

Zirconium loaded DKSC (Zr-DKSC); Fig. 7.3(b) shows the IR spectra of the zirconium-

loaded DKSC. After biosorption, five sharp peaks were observed near the frequencies

2924.06, 2853.58, 1710.52, 1460.90, 1162.50cm-1. Similarly broad peaks were seen at

3354.82, 1633.14, 1527.03, 1373.73, 1028.14, 694.57, and 568.17cm-1. Two wide peaks

(initially at 3567.69 and 3448.96 cm-1) dissipated and a new peak emerges in the range

3354.82cm-1 due to the O-H stretching vibration of aliphatic groups such as alcohols and

111

carboxylic acids in cellulose and lignin after biosorption (Ai et al., 2013). The two sharp

peaks over 2924.06 and 2853.58cm-1 (previously at 2925.17 and 2854.28cm-1) were

due to C-H symmetrical stretching vibrations of the methyl group. A sharp peak shifted

from 1709.51 to 1710.52 cm-1 due to the –C=O stretching of COOH group. Also, a wide

peak has shifted to 1633.14 (previously at 1628.36cm-1) due to the asymmetric

stretching of C=O, C-O and O-H in the fingerprint region (Senthil Kumar et al., 2011).

Shifting of the peak from 1516.02 to 1527.03cm-1 resembles the asymmetric stretching

of a N-O nitro group. The sharp peak at 1460.18cm-1shifted from 1460.90cm-1 due to

C=O stretching vibration of the amide group. After biosorption, a new peak appeared at

1373.73 cm-1 due to a weak symmetrical stretching of the CH3 group. The sharp peak at

1028.15 cm-1becomes a broad peak after biosorption affecting the C-N stretching

vibration. Two new peaks appeared at 594.57 and 568.17cm-1 due to the asymmetric

stretching vibration of metal ion (Zr-O) and the stretched vibration of weakly bonded

oxygen with zirconium. From these observations, the active groups namely methyl,

carboxylic, amine and nitro groups played a major role in Zr(IV) metal biosorption onto

DKSC. Numerous authors have reported that these are the potential functional groups

that are responsible for binding metal ions in the biosorption process (Zolgharnein et

al., 2013, Duygu Ozdes 2010, Huseyin Serencam, 2014, Ruhan Altun Anayurt, 2009)).

Regenerated DKSC (R-DKSCTh and R-DKSCZr); Fig. 7.2 (c) and Fig. 7.3(c)show the

FTIR spectra of regenerated biomass obtained from thorium and zirconium desorption

process using HCl and NaHCO3 as eluents, respectively.The IR spectrum of R_DKSC (R-

DKSCTh and R-DKSCZr) is similar to that of native DKSC consisting of similar absorption

bands in the single and triple bonds region. Furthermore, most of the absorption bands

in the fingerprint region of the spectrum coincide with those of pure DKSC. These

112

observations establish an impression that the desorptionprocess used in this work

hasregenerated the biomass with characteristics that are similar to those of pure

biomass thereby allowing the regenerated biomass to be reused in multiple

biosorption/desorption process cycles.

Table 7.2: Comparision of Shifts in FTIR spectra.

Radionuclide Band (cm-1) Assignment

Th

3403.89 O-H Stretching

2924.61 Rocking aldehydic C-H

2851.54 Oscillation of alkanes C-H

1637.65 Rocking amides (-C=O) group

1517.20 Wobbling of amine (-NH)

1459.07 Rocking carboxylic groups (-COOH)

760.09 Stretching of C-H bonds of aromatics

719.94 Weak oxygen bond with thorium ions

Zr

3354.82 O-H stretching vibration of aliphatic groups

2924.06 and 2853.58 Rocking symmetrical stretching of methyl group

1710.52 Rocking –C=O of carboxylic group

1633.14 Asymmetric stretching of C=O, C-O and O-H

1527.03 Asymmetric stretching of N-O

1460.90 Rocking C=O

1373.73 Symmetrical stretching of CH3 group

1028.15 C-N stretching vibration

594.57 and 568.17 Weakly bonded oxygen atoms with Zr ions

7.3.3. Scanning Electron Microscopic (SEM) analysis

Scanning Electron Microscopic (SEM) analysis was carried out for the native and the

metal loaded DKSC to determine the surface morphology during biosorption process.

Fig. 7.4(a)–(d) show the micrographs of the native, pre-treated, and thorium and

zirconium loaded biomass, respectively. As seen from the images, the surface of raw

113

biomass (Fig. 7.4(a)) exhibits a solid structure with the completely folded non-

particulate surface. DKSC (physically treated) (Fig. 7.4(b)) exhibits a homogeneous

structure with deep pores, which indicates that the pretreatment method has enhanced

the porous nature thereby increasing the surface area of the sorbent. On the other hand,

metal-loaded biomass samples (Fig. 7.4 (c) and 7.4 (d)) exhibit a non-folded porous

structure formed due to its hydrophilic nature.

Figure 7.4: SEM micrographs of DKSC. (a) Raw biomass, (b) DKSC (after pretreatment),

(c) Th-DKSC and (d) Zr-DKSC..

114

7.4. Conclusions

The characterisation studies of deoiled Karanja biomass have revealed that the physical

pre-treatment followed has enhanced the surface area of the biomass. This was

confirmed by SEM analysis. FTIR analysis confirmed that the hydroxyl, carboxyl, and

amine are the major functional groups participated in the metal complexation reactions

during biosorption. Furthermore, the regenerated biomass has characteristics that are

similar to those of native biomass leading to the conclusion that desorption process has

regenerated the biomass so that it can be recycled further.

115

Chapter 8 Conclusions and

Recommendations

116

The research work presented here was about the development of a treatment process

that can be used for the removal and recovery of radionuclides (Th and Zr) via a

biosorption/desorption method. Literature suggests that extensive investigations are

required for recognising relatively suitable biosorbents capable of separating significant

quantities of radionuclide ions from aqueous solutions. Recently, agricultural by-

products and plant wastes have been identified as important and economic sources to

be used as biosorbents. DKSC is one such agricultural waste that can be used as a

biosorbent because it is cheap, reusable, environmentally friendly and simple to use,

especially for the removal of Th and Zr ions from contaminated sites.

A simple pretreatment method was adopted to enhance the ability of the DKSC

biomass to be used as a biosorbent. The property of the new biomass was investigated

using characterisation techniques such as SEM, FTIR, EDX, pHpzc and methods for

characterising the physicochemical properties. The characterisation techniques (SEM

and FTIR) have revealed that the pre-treatment method adopted has enhanced the

surface area and porosity of the biomass. FTIR analysis also showed that the functional

groups, namely amines, alcoholic, carboxylic, amide and nitro groups present on the

DKSC surface participated in the biosorption process by strong complexation reactions

and weak electrostatic forces. The research work mainly dealt with the process

variables (initial ion concentration, initial pH, sorbent loading) that affect the

biosorption/desorption studies. These variables were optimised for achieving the

maximum efficiency using DOE concepts such as the Taguchi OA (L16 and L18) and Box-

Behnken methods in RSM with the desirability approach used in the concentrations

considered. Also, equilibrium, kinetic and thermodynamic data have been evaluated to

determine the various mechanisms of the sorption process.

117

Thorium Biosorption; The efficiency of DKSC as a sorbent in removing thorium ions

from aqueous solutions was studied by a batch biosorption technique, and process

variables were optimised by employing a Taguchi robust OA design approach (L16

(43)). The responses qe and R% were optimised for their maximum values by adapting

the desirability function in the multivariate response optimisation. The optimum

conditions were determined to be an initial Th(IV) concentration at level 4 (130 mg/L),

an initial pH at level 4 (5) and a DKSC loading at level 1 (0.25 g/L). These conditions

gave a maximum value of 260 mg/g for qe and 99.97% for R%. The Langmuir isotherm

model was found to exhibit a good correlation for the equilibrium data. The kinetic

study revealed that the data fitted best to a pseudo-second order model signifying that

the overall rate of biosorption was affected by chemisorption in the concentration range

used in this work. Thermodynamic studies showed that the biosorption process was

spontaneous and endothermic.

Zirconium biosorption; The effectiveness of DKSC as a biosorbent for the biosorption

of Zr(IV) from aqueous solutions was tested by a batch biosorption technique. The most

influential process variables were studied according to a Box-Behnken design in RSM

and were optimised by using a desirability approach in a multi-response optimisation.

Regression analysis showed that a full-quadratic model provided a good fit to the

experimental data with a coefficient of determination (𝑅2) value of 0.98 and an𝐹-Value

of 26.96. Optimisation studies showed that the combination of process variables

including the initial Zr concentration of 74.99 mg/L, an initial pH of 3.57 and a DKSC

loading of 3.00 g/L led to a maximum response of 97.8% for R% and 23.44 mg/g for qe

at a desirability value of 0.99. ANOVA revealed that pH and DKSC loading were the most

influential process variables in Zr(IV) biosorption using DKSC. Equilibrium data fitted

118

best to a Freundlich isotherm model and kinetic data followed pseudo-second-order

model in the range of concentration investigated.

Desorption process; Desorption studies is an extension to biosorption method

employed for the recovery of bound ions from loaded biomass. It is a useful step for the

regeneration of sorbent. The process variables that affect the recovery of these species

were found to be L/S ratio, eluent type, and concentration. Therefore, the variables

above were optimised using a Taguchi mixed level design 𝐿18 (2132) OA for achieving a

maximum D%. The results have shown that 1M HCl and 0.1M NaHCO3 can be used for

the effective recovery of Th and Zr from loaded biomass with a maximum 𝐷% of 96%

and 69% at L/S ratios of 7 and 3 for the desorption of thorium and zirconium,

respectively. The desorption trend followed pseudo-second order kinetics for both Th

and Zr. Also, the regenerated biomass (DKSCR) exhibited similar characteristics as those

of native DKSC which was confirmed through the determination of various

physicochemical properties and FTIR analysis thereby confirming the possibility of

reutilisation of DKSC in multiple cycles.

Overall Outcomes

• Based on the results obtained in this work, it can be concluded that DKSC can

be effectively used as a natural and economic biosorbent for the removal and

recovery of thorium and zirconium ions from aqueous streams and is

therefore suitable as a low-cost biosorbent in the sequestration of

radionuclides from effluents produced in nuclear and hydrometallurgical

industries.

119

Limitations

• The present research is limited to the regeneration of the loaded biomass in a

single cycle only. The recycling or reuse of the regenerated biomass using

more cycles is beyond the scope of the current work.

Recommendations

• Identification of different biomass materials similar to DKSC and their

utilisation as biosorbents in the pollution remediation processes.

• Scale-up studies for the commercialisation of biosorption process based on

the availability, costs, and uptake capacity of the biomass.

• Investigating the biosorption processes for the treatment of effluent streams

containing multi-ions.

• Continuous biosorption studies using packed and fluidised bed.

120

APPENDIX-A Table A1: Effect of contact time (preliminary studies)

Time, minutes Ct, mg/L qt, mg/g R%

0 28.2 0 0 35 5 23.2 82.2695

135 3.9 24.3 86.17021 165 3.6 24.6 87.23404 195 3.6 24.6 87.23404 225 3.5 24.7 87.58865 255 3.3 24.9 88.29787 285 2.3 25.9 91.84397 315 2.3 25.9 91.84397

Table A2: Equilibrium data for thorium biosorption studies

Ci (mg/L) Ce (mg/L) qe (mg/g) Kd (L/g) RL Ɵ R%

25 1.78 23.22 13.04494 0.187529 0.769186 92.88 100 8.9 91.1 10.23596 0.065232 0.930216 91.1 150 46.9 103.1 2.198294 0.045465 0.95237 68.733 200 99.6 100.4 1.008032 0.034892 0.963847 50.2 465 353.1 111.9 0.316907 0.015629 0.984123 24.06 530 424.3 105.7 0.249116 0.013765 0.986043 19.94 625 512.5 112.5 0.219512 0.011722 0.988139 18 700 585.2 114.8 0.196172 0.010492 0.989397 16.4

Table A3:qe values calculated from isotherm models

Experimental data

Langmuir model

Freundlich model

Ce, mg/L qe, mg/g qe qe 1.78 23.22 23.98 39.05 8.9 91.1 67.83 54.39

46.9 103.1 107.77 76.60 99.6 100.4 116.24 89.46

353.1 111.9 122.40 116.11 424.3 105.7 122.83 120.59 512.5 112.5 123.19 125.37 585.5 114.8 123.42 128.84

121

Table A4: Thermodynamic studies (effect of temperature)

Temperature, K Ci, mg/L Ce, mg/L qe, mg/g Kd, L/g B% ∆𝑮 288.15 100 10.721 177.6697 16.57212 89.28 -6726.4 298.15 100 4.944 179.69 36.34506 95.06 -8906.54 308.15 100 1.769 195.6793 110.6158 98.23 -12056.7 318.15 100 8.293 1392.508 167.9137 91.71 -13552

122

Appendix-B Table B1: Effect of contact time in Zr biosorption

Time, minutes

Ci, mg/L R%

0 48.42 0 15 38.1 21.31351 30 35.66 26.35275 60 28.3 41.55308

120 24.72 48.94672 150 24.4 49.6076 200 22.86 52.7881 220 23.3 51.87939 240 23.24 52.0033

Table B2: Equilibrium data for zirconium biosorption studies

Ci, mg/L Ce, mg/L qe, mg/g Kd, L/g 18.3 1.96 5.41 2.76

22.54 2.67 6.60 2.48 28.42 3.76 8.19 2.18 35.02 4.97 9.99 2.01 44.64 6.74 12.54 1.86 55.05 8.53 15.49 1.82 60.10 9.57 16.81 1.75 67.44 11.05 18.76 1.69 80.08 13.27 22.17 1.67 89.24 15.47 24.47 1.58

Table B3:qe values calculated from adsorption isotherm models

Experimental data

Langmuir model

Freundlich model

DR Temkin

Ce, mg/L qe, mg/g qe, mg/g qe, mg/g qe, mg/g qe, mg/g 1.96 5.41 5.17 5.19 4.24 0.49 2.67 6.60 6.72 6.52 7.59 0.52 3.76 8.19 8.83 8.41 11.18 0.55 4.97 9.99 10.87 10.34 13.47 0.58 6.74 12.54 13.39 12.97 15.22 0.61 8.53 15.49 15.51 15.44 16.13 0.64 9.57 16.81 16.59 16.82 16.47 0.65

11.05 18.76 17.96 18.71 16.80 0.66

123

13.27 22.17 19.72 21.44 17.12 0.68 15.47 24.47 21.18 24.02 17.32 0.69

Table B4:qt values calculated from kinetic models

Experimental Data

Pseudo-first order

Pseudo-second order

Intraparticle diffusion

Elovich

Time, minutes

qt, mg/g

qt, mg/g

qt, mg/g

qt, mg/g

qt, mg/g

0 0 2.653314 0 4.194 0 15 4.053333 2.820173 4.846527 5.104151 4.711812 30 6.106667 2.97267 6 5.481148 5.479126 60 6.746667 3.239419 6.810443 6.014302 6.246439

120 6.866667 3.64833 7.303713 6.768296 7.013753 180 7.253333 3.933618 7.484407 7.346856 7.462603 210 7.56 4.04207 7.537688 7.599474 7.633248 240 7.793333 4.132656 7.57815 7.834604 7.781067 270 7.866667 4.208321 -- 8.055444 7.911453

Table B5: Thermodynamic studies for Zr biosorption

Temperature, K qe, mg/g LnKd ∆𝑮, kJ/mole 288 1.49 -3.89 8.79 298 7.79 -2.16 5.08 308 34.62 0.39 1.37 318 33.47 0.29 -2.35

124

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